Our concern in this paper is to:
provide a comprehensive review of recent off-line and on-line trends in Arabic cursive handwriting recognition (last 10 years publications)
clarify the challenges standing against obtaining a reliable, accurate, simple, general purpose recognizer based on these trends.
Arabic Handwritten Text Recognition and Writer IdentificationMustafa Salam
A seminar of Ph.D. theses which explain a proposed system for recognize the Arabic handwritten text and identify the text writer. Several proposed steps are described in details in this seminar and the obtained results are viewed in detail.
offline character recognition for handwritten gujarati textBhumika Patel
This document summarizes a presentation on optical character recognition of Gujarati characters using convolutional neural networks. It outlines collecting a dataset of 1360 images each of 34 Gujarati characters written by different people. The proposed approach involves preprocessing images, training a CNN model, and calculating accuracy. Initial results correctly recognized some characters but had difficulty with connected characters. Future work includes recognizing remaining characters and vowels, collecting more data, and exploring different CNN configurations to improve accuracy.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Optical character recognition of handwritten Arabic using hidden Markov modelsMuhannad Aulama
The document describes an approach to optical character recognition of handwritten Arabic text using hidden Markov models. It discusses extracting optical features from Arabic characters like width-to-length ratios and number of dots. It also describes encoding the structure of the Arabic language by analyzing character transition probabilities and frequencies. Hidden Markov models are used to model both the optical properties and language structure. Recognition is performed using the Viterbi algorithm to identify the most probable characters. The approach achieved accurate recognition of handwritten Arabic text.
Support Vector Machine ppt presentationAyanaRukasar
Support vector machines (SVM) is a supervised machine learning algorithm used for both classification and regression problems. However, it is primarily used for classification. The goal of SVM is to create the best decision boundary, known as a hyperplane, that separates clusters of data points. It chooses extreme data points as support vectors to define the hyperplane. SVM is effective for problems that are not linearly separable by transforming them into higher dimensional spaces. It works well when there is a clear margin of separation between classes and is effective for high dimensional data. An example use case in Python is presented.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Arabic Handwritten Text Recognition and Writer IdentificationMustafa Salam
A seminar of Ph.D. theses which explain a proposed system for recognize the Arabic handwritten text and identify the text writer. Several proposed steps are described in details in this seminar and the obtained results are viewed in detail.
offline character recognition for handwritten gujarati textBhumika Patel
This document summarizes a presentation on optical character recognition of Gujarati characters using convolutional neural networks. It outlines collecting a dataset of 1360 images each of 34 Gujarati characters written by different people. The proposed approach involves preprocessing images, training a CNN model, and calculating accuracy. Initial results correctly recognized some characters but had difficulty with connected characters. Future work includes recognizing remaining characters and vowels, collecting more data, and exploring different CNN configurations to improve accuracy.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Optical character recognition of handwritten Arabic using hidden Markov modelsMuhannad Aulama
The document describes an approach to optical character recognition of handwritten Arabic text using hidden Markov models. It discusses extracting optical features from Arabic characters like width-to-length ratios and number of dots. It also describes encoding the structure of the Arabic language by analyzing character transition probabilities and frequencies. Hidden Markov models are used to model both the optical properties and language structure. Recognition is performed using the Viterbi algorithm to identify the most probable characters. The approach achieved accurate recognition of handwritten Arabic text.
Support Vector Machine ppt presentationAyanaRukasar
Support vector machines (SVM) is a supervised machine learning algorithm used for both classification and regression problems. However, it is primarily used for classification. The goal of SVM is to create the best decision boundary, known as a hyperplane, that separates clusters of data points. It chooses extreme data points as support vectors to define the hyperplane. SVM is effective for problems that are not linearly separable by transforming them into higher dimensional spaces. It works well when there is a clear margin of separation between classes and is effective for high dimensional data. An example use case in Python is presented.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Handwriting Recognition Using Deep Learning and Computer VersionNaiyan Noor
This document presents a method for handwriting recognition using deep learning and computer vision. It discusses preprocessing images by removing noise and converting to grayscale. Thresholding is used to separate darker text pixels from lighter background pixels. The image is then segmented into individual lines and words. Python libraries like TensorFlow, Spyder and Jupyter Notebook are used. The goal is to build a system that can recognize text in images and display the text to users. Future work may include recognizing cursive text and additional languages.
This document describes a technique for Sinhala handwritten character recognition using feature extraction and an artificial neural network. The methodology includes preprocessing, segmentation, feature extraction based on character geometry, and classification using an ANN. Features like starters, intersections, and zoning are extracted from segmented characters. The ANN was trained on these feature vectors and tested on 170 characters, achieving an accuracy of 82.1%. While the technique showed some success, the author notes room for improvement, such as making the system more font-independent and improving feature extraction and character separation.
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This document discusses support vector machines (SVM) and provides an example of using SVM for classification. It begins with common applications of SVM like face detection and image classification. It then provides an overview of SVM, explaining how it finds the optimal separating hyperplane between two classes by maximizing the margin between them. An example demonstrates SVM by classifying people as male or female based on height and weight data. It also discusses how kernels can be used to handle non-linearly separable data. The document concludes by showing an implementation of SVM on a zoos dataset to classify animals as crocodiles or alligators.
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
This document provides an introduction to artificial intelligence. It discusses four views of AI: thinking like humans through cognitive modeling; acting like humans by passing the Turing test; thinking rationally through logical reasoning; and acting rationally by maximizing goals. The document also summarizes the history of AI from its foundations in philosophy, mathematics, and other fields to modern achievements like Deep Blue beating Kasparov at chess. It concludes with examples of state-of-the-art AI systems being used for logistics planning, spacecraft scheduling, and solving crossword puzzles.
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
The document describes two feature extraction methods: attention based and statistics based. The attention based method models how human vision finds salient regions using an architecture that decomposes images into channels and creates image pyramids, then combines the information to generate saliency maps. This method was applied to face recognition but had problems with pose and expression changes. The statistics based method aims to select a subset of important features using criteria based on how well the features represent the original data.
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are modeled after biological neural networks and neurons. The key concepts covered include the basic structure and functioning of artificial neurons, different types of learning in ANNs, commonly used network architectures, and applications of ANNs. Examples of applications discussed are classification, recognition, assessment, forecasting and prediction. The document also notes how ANNs are used across various fields including computer science, statistics, engineering, cognitive science, neurophysiology, physics and biology.
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn
The document discusses decision trees and how they work. It begins with explaining what a decision tree is - a tree-shaped diagram used to determine a course of action, with each branch representing a possible decision. It then provides examples of using a decision tree to classify vegetables and animals based on their features. The document also covers key decision tree concepts like entropy, information gain, leaf nodes, decision nodes, and the root node. It demonstrates how a decision tree is built by choosing splits that maximize information gain. Finally, it presents a use case of using a decision tree to predict loan repayment.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
This presentation about Scikit-learn will help you understand what is Scikit-learn, what can we achieve using Scikit-learn and a demo on how to use Scikit-learn in Python. Scikit is a powerful and modern machine learning python library. It's a great tool for fully and semi-automated advanced data analysis and information extraction. There are a lot of reasons why Scikit-Learn is a preferred machine learning tool. It has efficient tools to identify and organize problems, such as whether it fits a supervised or unsupervised learning model. It contains many free and open data sets. It has a rich set of built-in libraries for learning and predicting. It provides model support for every problem type. It also has built-in functions such as pickle for model persistence. It is supported by a huge open source community and vendor base. Now, let us get started and understand Sciki-Learn in detail.
Below topics are explained in this Scikit-Learn presentation:
1. What is Scikit-learn?
2. What we can achieve using Scikit-learn
3. Demo
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands-on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
This document provides an overview of machine learning. It defines machine learning as a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. The document then discusses why machine learning is important, how it works by exploring data and identifying patterns with minimal human intervention, and provides examples of machine learning applications like autonomous vehicles. It also summarizes the main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and deep learning. Finally, it distinguishes machine learning from deep learning and defines data science.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
Off-line English Character Recognition: A Comparative Surveyidescitation
It has been decades since the evolution of idea that
human brain can be mimicked by artificial neuron like
mathematical structures. Till date, the development of this
endeavor has not reached the threshold of excellence. Neural
networks are commonly used to solve sample-recognition
problems. One of these is character recognition. The solution
of this problem is one of the easier implementations of neural
networks. This paper presents a detailed comparative
literature survey on the research accomplished for the last
few decades. The comparative literature review will help us
understand the platform on which we stand today to achieve
the highest efficiency in terms of Character Recognition
accuracy as well as computational resource and cost.
Handwriting Recognition Using Deep Learning and Computer VersionNaiyan Noor
This document presents a method for handwriting recognition using deep learning and computer vision. It discusses preprocessing images by removing noise and converting to grayscale. Thresholding is used to separate darker text pixels from lighter background pixels. The image is then segmented into individual lines and words. Python libraries like TensorFlow, Spyder and Jupyter Notebook are used. The goal is to build a system that can recognize text in images and display the text to users. Future work may include recognizing cursive text and additional languages.
This document describes a technique for Sinhala handwritten character recognition using feature extraction and an artificial neural network. The methodology includes preprocessing, segmentation, feature extraction based on character geometry, and classification using an ANN. Features like starters, intersections, and zoning are extracted from segmented characters. The ANN was trained on these feature vectors and tested on 170 characters, achieving an accuracy of 82.1%. While the technique showed some success, the author notes room for improvement, such as making the system more font-independent and improving feature extraction and character separation.
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This document discusses support vector machines (SVM) and provides an example of using SVM for classification. It begins with common applications of SVM like face detection and image classification. It then provides an overview of SVM, explaining how it finds the optimal separating hyperplane between two classes by maximizing the margin between them. An example demonstrates SVM by classifying people as male or female based on height and weight data. It also discusses how kernels can be used to handle non-linearly separable data. The document concludes by showing an implementation of SVM on a zoos dataset to classify animals as crocodiles or alligators.
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
This document provides an introduction to artificial intelligence. It discusses four views of AI: thinking like humans through cognitive modeling; acting like humans by passing the Turing test; thinking rationally through logical reasoning; and acting rationally by maximizing goals. The document also summarizes the history of AI from its foundations in philosophy, mathematics, and other fields to modern achievements like Deep Blue beating Kasparov at chess. It concludes with examples of state-of-the-art AI systems being used for logistics planning, spacecraft scheduling, and solving crossword puzzles.
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
The document describes two feature extraction methods: attention based and statistics based. The attention based method models how human vision finds salient regions using an architecture that decomposes images into channels and creates image pyramids, then combines the information to generate saliency maps. This method was applied to face recognition but had problems with pose and expression changes. The statistics based method aims to select a subset of important features using criteria based on how well the features represent the original data.
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are modeled after biological neural networks and neurons. The key concepts covered include the basic structure and functioning of artificial neurons, different types of learning in ANNs, commonly used network architectures, and applications of ANNs. Examples of applications discussed are classification, recognition, assessment, forecasting and prediction. The document also notes how ANNs are used across various fields including computer science, statistics, engineering, cognitive science, neurophysiology, physics and biology.
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn
The document discusses decision trees and how they work. It begins with explaining what a decision tree is - a tree-shaped diagram used to determine a course of action, with each branch representing a possible decision. It then provides examples of using a decision tree to classify vegetables and animals based on their features. The document also covers key decision tree concepts like entropy, information gain, leaf nodes, decision nodes, and the root node. It demonstrates how a decision tree is built by choosing splits that maximize information gain. Finally, it presents a use case of using a decision tree to predict loan repayment.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
This presentation about Scikit-learn will help you understand what is Scikit-learn, what can we achieve using Scikit-learn and a demo on how to use Scikit-learn in Python. Scikit is a powerful and modern machine learning python library. It's a great tool for fully and semi-automated advanced data analysis and information extraction. There are a lot of reasons why Scikit-Learn is a preferred machine learning tool. It has efficient tools to identify and organize problems, such as whether it fits a supervised or unsupervised learning model. It contains many free and open data sets. It has a rich set of built-in libraries for learning and predicting. It provides model support for every problem type. It also has built-in functions such as pickle for model persistence. It is supported by a huge open source community and vendor base. Now, let us get started and understand Sciki-Learn in detail.
Below topics are explained in this Scikit-Learn presentation:
1. What is Scikit-learn?
2. What we can achieve using Scikit-learn
3. Demo
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands-on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
This document provides an overview of machine learning. It defines machine learning as a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. The document then discusses why machine learning is important, how it works by exploring data and identifying patterns with minimal human intervention, and provides examples of machine learning applications like autonomous vehicles. It also summarizes the main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and deep learning. Finally, it distinguishes machine learning from deep learning and defines data science.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
Off-line English Character Recognition: A Comparative Surveyidescitation
It has been decades since the evolution of idea that
human brain can be mimicked by artificial neuron like
mathematical structures. Till date, the development of this
endeavor has not reached the threshold of excellence. Neural
networks are commonly used to solve sample-recognition
problems. One of these is character recognition. The solution
of this problem is one of the easier implementations of neural
networks. This paper presents a detailed comparative
literature survey on the research accomplished for the last
few decades. The comparative literature review will help us
understand the platform on which we stand today to achieve
the highest efficiency in terms of Character Recognition
accuracy as well as computational resource and cost.
A Semi-Automatic Annotation Tool For Arabic Online Handwritten TextRanda Elanwar
Presentation of PhD dissertation
Content
Text Lines Extraction using dynamic programming
Words Extraction using SVM and RBF
Words Segmentation using HMM
User Interfaces on Matlab
Annotation performance evalution
Holistic Approach for Arabic Word RecognitionEditor IJCATR
Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the
most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large
training dataset, researchers currently study Holistic Approach “recognition of a word without segmentation”. This paper describes a
method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each
Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also
show that best number of state for each word need careful selection and considerations.
ICFHR 2014 Competition on Handwritten KeyWord Spotting (H-KWS 2014)Konstantinos Zagoris
H-KWS 2014 is the Handwritten Keyword Spotting Competition organized in conjunction with ICFHR 2014 conference. The main objective of the competition is to record current advances in keyword spotting algorithms using established performance evaluation measures frequently encountered in the information retrieval literature. The competition comprises two distinct tracks, namely, a segmentation-based and a segmentation- free track. Five (5) distinct research groups have participated in the competition with three (3) methods for the segmentation- based track and four (4) methods for the segmentation-free track. The benchmarking datasets that were used in the contest contain both historical and modern documents from multiple writers. In this paper, the contest details are reported including the evaluation measures and the performance of the submitted methods along with a short description of each method.
Segmentation - based Historical Handwritten Word Spotting using document-spec...Konstantinos Zagoris
Many word spotting strategies for the modern documents are not directly applicable to historical handwritten documents due to writing styles variety and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that relies upon document-specific local features which take into account texture information around representative keypoints. Experimental work on two historical handwritten datasets using standard evaluation measures shows the improved performance achieved by the proposed methodology.
The document proposes a system to identify six scripts including Arabic, Chinese, Cyrillic, Devnagari, Japanese, and Roman. It extracts 11 spatial and temporal features from word strokes and achieves 87.1% accuracy at the word level using 5-fold cross validation. Accuracy improves to 95% for 5-word samples and 95.5% for full text lines containing an average of 7 words. The system allows analyzing individual strokes and uses spatial and temporal information to identify scripts.
Performance of Statistics Based Line Segmentation System for Unconstrained H...AM Publications
Handwritten character recognition is a technique by which a computer system could recognize characters and other symbols written in natural handwriting. Segmentation decomposes the document image into subcomponents like lines, words and characters. To achieve greater accuracy, segmentation and recognition could not be treated independently. Most of the existing line segmentation methods have limitations when applied to unconstrained handwritten documents. Statistics based line segmentation system was developed in Java Developer Kit 1.6 for segmenting unconstrained handwritten document images into lines. Arithmetic mean, trimmed mean and inter-quartile mean were used appropriately to achieve accurate segmentation results. The performance of the system was studied by using a few public handwritten document image datasets and images collected from different writers to compare its segmentation accuracy. The datasets contained well separated, sharing, touching, overlapping, irregular base and short handwritten text lines. The samples from the datasets were also segmented by a few other line segmentation methods. The segmentation accuracy of the system was higher than that of other methods. Performance measures like language support, segmentation document and line type of the system were compared with that of other line segmentation methods. The developed system segmented handwritten and printed lines from English, Chinese and Bengali languages and supported linear and non linear lines.
Scalability in Model Checking through Relational DatabasesCSCJournals
This document discusses an ATL model checking tool that uses relational databases to improve scalability. The tool allows designers to interactively build models as directed graphs and verify properties expressed as ATL formulas. The key contributions are implementing the Pre function using relational algebra expressions translated to SQL, and generating an ATL model checker from a grammar specification using ANTLR. Relational databases improve performance by efficiently representing large models for verification.
The document outlines the main stages of image processing which include image acquisition, restoration, enhancement, representation and description, segmentation, object recognition, color processing, compression, and morphological operations. It describes each stage in detail, explaining their purposes and some common techniques used. The overall stages take a raw image and perform various operations to extract useful information and simplify analysis for applications like object identification and extraction.
Usage of Shape From Focus Method For 3D Shape Recovery And Identification of ...CSCJournals
Shape from focus is a method of 3D shape and depth estimation of an object from a sequence of pictures with changing focus settings. In this paper we propose a novel method of shape recovery, which was originally created for shape and position identification of glass pipette in medical hybrid robot. In proposed algorithm, Sum of Modified Laplacian is used as a focus operator. Each step of the algorithm is tested in order to pick the operators with the best results. Reconstruction allows not only to determine shape but also precisely define position of the object. The results of proposed method, performed on real objects, have shown the efficiency of this scheme.
Feature Analysis for Affect Recognition Supporting Task Sequencing in Adaptiv...janningr
Originally, the task sequencing in adaptive intelligent tutoring systems needs information gained from expert and domain knowledge as well as information about former performances. In a former work a new efficient task sequencer based on a performance prediction system was presented, which only needs former performance information but not the expensive expert and domain knowledge. This task sequencer uses the output of the performance prediction to sequence the tasks according to the theory of Vygotsky’s Zone of Proximal Development. In this presentation we aim to support this sequencer by a further automatically to gain information source, namely speech input from the students interacting with the tutoring system. The proposed approach extracts features from students speech data and applies to that features an automatic affect recognition method. The output of the affect recognition method indicates, if the last task was too easy, too hard or appropriate for the student. In this presentation we (1) propose a new approach for supporting task sequencing by affect recognition, (2) present an analysis of appropriate features for affect recognition extracted from students speech input and (3) show the suitability of the proposed features for affect recognition for supporting task sequencing in adaptive intelligent tutoring systems.
Image Enhancement by Image Fusion for Crime InvestigationCSCJournals
This document proposes a method for image enhancement through image fusion for crime investigation applications. It summarizes existing image enhancement techniques like histogram equalization and presents their limitations. It then describes the proposed method which involves constructing an image pyramid and performing a wavelet transformation on input images. The pyramid and wavelet transformed images are then fused to generate an enhanced output image with improved contrast and information content. Experimental results on a surveillance camera image show that the proposed fusion scheme provides better perception for human visual analysis compared to traditional enhancement techniques.
Fourth Dimension Level 1 By Dr.Moiz HussainEhtesham Mirxa
The Fourth Dimension® Educational series
Conceived, Created and Conducted by Prof. Dr Moiz Hussain
Motive ......
“To create a world of those who can alter the reality of the existing world to make it a better place to live and for those who follow”
Level-1
THE AWAKENING
From the Impossible to the POSSIBLE
(Conducted in Pakistan, India, UAE, USA, UK, Germany, Austria, Switzerland and KSA)
The Fourth Dimension® is one of the most powerful and result oriented workshop that can change your life from a level of ordinary to outstanding. Based on programming the sub conscious mind to attract health, wealth and happiness in your life through the learning and use of directed day dreaming and creative visualization in area such as;
• Education-learning, higher grades
• Improved memory and concentration
• Decision making
• Out of Box thinking
• Intuition and sixth sense development
• Creativity, imagination and visualization of goals
• Goal setting and goal achievement
• Self confidence and personal charisma
• Prevention and healing of diseases, disorders and many health conditions
• Improved relationship and quality in relationships-happiness
• Success in Business, job and career
• Those suffering from Panic attack, anxiety, Depression, Stress,
• Low self esteem, Lack of Confidence
• Weak memory and concentration
• Restful sleep
• Anti aging
• Finding love and the right partner
• Attracting Wealth in your business
• Creating massive unprecedented success in your professional and personal life
• Any much much more
Who should attend?
Business executives, employees and professionals
Entrepreneurs & Leaders
Students
Housewives
Doctors, Psychologist, Psychiatrist, Occupational Therapist
Wealth management Managers
Police, Military and law and order enforcement agencies
Just anyone who wants success and wants to achieve the impossible
Requirements
Take a High Protein Breakfast when you come for the workshop (eggs, cheese, meat, poultry)
Wear loose clothing
Bring 4 passport size colored photographs
Sign the code of ethic agreement
Recording and note taking is not allowed, cell phones must be switched off during workshop
Those who have attended the Fourth Dimension workshop includes:
Senior Doctors, Psychiatrist and Psychologists
Business tycoons, Stock management managers, brokers
Senior and middle management
Decision makers including presidents of multinational companies, Banks
Govt, Police and senior Military officials
Students of O and A Levels, College and University students.
Scientists, Research scholars, Artists , Musicians and singers
TV and Film actors and actress
Leaders and Entrepreneurs
Improving the Accuracy of Object Based Supervised Image Classification using ...CSCJournals
A lot of research has been undertaken and is being carried out for developing an accurate classifier for extraction of objects with varying success rates. Most of the commonly used advanced classifiers are based on neural network or support vector machines, which uses radial basis functions, for defining the boundaries of the classes. The drawback of such classifiers is that the boundaries of the classes as taken according to radial basis function which are spherical while the same is not true for majority of the real data. The boundaries of the classes vary in shape, thus leading to poor accuracy. This paper deals with use of new basis functions, called cloud basis functions (CBFs) neural network which uses a different feature weighting, derived to emphasize features relevant to class discrimination, for improving classification accuracy. Multi layer feed forward and radial basis functions (RBFs) neural network are also implemented for accuracy comparison sake. It is found that the CBFs NN has demonstrated superior performance compared to other activation functions and it gives approximately 3% more accuracy.
This paper presents to building identification from satellite images. Because of monitoring illegal land usage. Nowadays rapid urbanization leads to
increase the land usage, in this case of monitoring illegal land usage is very important. This project implemented to building identification from
satellite images, images are provided from Bing maps. Adaptive Neuro Fuzzy Inference System used to check data base information. In this proposed
system, I can identify only building images from the satellite images, To improving the image details effectively.
Video Key-Frame Extraction using Unsupervised Clustering and Mutual ComparisonCSCJournals
The document presents a novel method for extracting key frames from videos using unsupervised clustering and mutual comparison. It assigns weights of 70% to color (HSV histogram) and 30% to texture (GLCM) when computing frame similarity for clustering. It then performs mutual comparison of extracted key frames to remove near duplicates, improving accuracy. The algorithm is computationally simple and able to detect unique key frames, improving concept detection performance as validated on open databases.
Vehicle accidents are one of the most leading causes of fatality. More accidents are because of improper driving of vehicles by non-licensed persons. One approach to eliminate the authentication of vehicles by non-licensed persons is to use a smart vehicle authentication technique for vehicles. This authentication is done by the fingerprint scanner which is the most secure system for vehicle and the smart license card which has the biometric details in it, the vehicle will start if the details in both the license and the fingerprint matches else it won’t. This will stop the authentication of vehicles by non-licensed persons. Another problem is that the many person are not aware of their vehicle insurance due dates. The most popular existing system to monitor the due dates is by the GSM technique but it has lots of disadvantages. To eliminate those disadvantages the Wi-Fi system is used to send intimations. So that the person can pay the due on date without any penalty.
AI and ML Skills for the Testing World TutorialTariq King
Software continues to revolutionize the world, impacting nearly every aspect of our work, family, and personal life. Artificial intelligence (AI) and machine learning (ML) are playing key roles in this revolution through improvements in search results, recommendations, forecasts, and other predictions. AI and ML technologies are being used in platforms for digital assistants, home entertainment, medical diagnosis, customer support, and autonomous vehicles. Testing practitioners are recognizing the potential for advances in AI and ML to be leveraged for automated testing—an area that still requires significant manual effort. Tariq King and Jason Arbon introduce you to the world of AI for software testing. Learn the fundamentals behind autonomous and intelligent agents, ML approaches including Bayesian networks, decision tree learning, neural networks, and reinforcement learning. Discover how to apply these techniques to common testing tasks such as identifying testable features, generating test flows, and detecting erroneous states.
2013-1 Machine Learning Lecture 01 - Pattern RecognitionDongseo University
This document provides an introduction to pattern recognition. It discusses philosophical debates around AI, definitions of key terms like features and patterns, and the typical components and design cycle of a pattern recognition system. The document also covers categories of pattern recognition problems like classification, regression, and clustering. It describes common classifiers and how to evaluate performance. Finally, it outlines major approaches to pattern recognition like template matching, statistical and neural network methods, and lists application areas such as character recognition, biometrics, medical diagnosis, and security.
This document provides an overview of machine learning, including:
1) It defines machine learning as teaching computational machines to solve problems by giving examples to automatically infer rules for associating inputs and outputs.
2) It discusses different machine learning algorithms like linear classifiers, support vector machines, ensemble methods, and deep learning.
3) It emphasizes the need for scalable deployment of machine learning models to handle large and streaming data, covering approaches like distributed and parallel processing using MapReduce and cloud services.
IRJET - Cognitive based Emotion Analysis of a Child Reading a BookIRJET Journal
1. The document describes a system to analyze the emotions of children while reading by capturing their facial expressions using CNN and analyzing the sentiment of the text using techniques like SVM.
2. The system aims to classify sentences based on emotions like joy, anger, fear etc. using models trained on Twitter data and detect facial expressions using a CNN model trained on a children's facial expression dataset.
3. The proposed application will record a child's face and text they are reading to analyze emotions over time and log the results to help understand responses to different parts of books or educational material.
The document summarizes and compares different methods for face recognition, including Eigenface, Line Edge Map (LEM), and other techniques. It provides descriptions of how each technique works, such as using eigenvectors to extract features for Eigenface. Experimental results show LEM achieves better accuracy than Eigenfaces for variations in lighting and size. While Eigenfaces struggles with size changes, LEM maintains high accuracy for different conditions. The document recommends future work combining techniques to maximize recognition accuracy.
AI driven classification framework for advanced Test AutomationSTePINForum
by Shubhradeep Nandi, Head of Digital, MSys Technologies at STeP-IN SUMMIT 2018 15th International Conference on Software Testing on August 31, 2018 at Taj, MG Road, Bengaluru
Dear Sir/Ma’am
I am interested to work as a data specialist in your organization. I believe my experience, skills and work attitude will aid your organization in a great way. Please accept my enclosed resume with this letter.
I worked at Accenture for the last four years. My key responsibilities here were to collect, analyse, store and create data. I made sure that these data were accurate and not damaged. As far as my educational background is concerned, I have a bachelor's degree in EXTC. I am excellent at solving problems and have great analytical skills. I am capable of working well with network administration and can explain the technical problems.
I would appreciate if we could meet up for an interview wherein we can discuss more on this. I can be contacted at +919493377607 or you can email me at imtiaz.khan.sw39@gmail.com
Thank You.
Yours sincerely,
Imtiaz Khan
This document discusses artificial intelligence (AI) and machine learning (ML). It defines AI as technologies that mimic human cognitive functions and ML as a type of AI that allows software to become more accurate without being explicitly programmed. The document outlines the differences between narrow and general AI, describes how QA professionals can test AI projects by gathering data, determining acceptance criteria statistically, and understanding the applications. It also defines supervised, unsupervised, and reinforcement learning and provides examples of common ML algorithms like linear regression, which is demonstrated through predicting a data point.
Top 50 Accenture Interview Questions and AnswersSimplilearn
This video is based on Accenture Interview for Freshers. In this video session, we will be covering various topics like Accenture core values, Accenture recruitment process, and various interview questions for freshers and experienced that will help you with your preparation. At last, we will cover some Accenture Interview Preparation Tips that will be a valuable resource for you while preparing for the interview.
🔥Explore Our Free Courses With Completion Certificate by SkillUp: https://www.simplilearn.com/skillup-f...
Accenture is a global professional services firm that specializes in consulting, technology, and outsourcing. The company was established in 1989 and is based in Dublin, Ireland. It operates in over 200 cities across 56 nations. Today it is one of the top IT firms in India and a global leader in management consulting, technology services, and outsourcing.
Accenture makes effective use of its industry knowledge and technical capabilities, identifying the most recent business and technology trends and delivering powerful solutions to help clients increase revenue. It works with 91 Fortune-100 global corporations and has been named to Fortune's "World's Most Admired Companies" list 19 times in a row.
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What are the Unique Challenges and Opportunities in Systems for ML?Matei Zaharia
Presentation by Matei Zaharia at the SOSP 2019 AI Systems workshop about the systems research challenges specific to machine learning systems, including debugging and performance optimization for ML. Covers research from Stanford DAWN and an industry perspective from Databricks.
Sahil Verma submitted an internship training report on Python facial emotion detection. The report introduced Freecodecamp, the internship platform, and discussed using convolutional neural networks and libraries like OpenCV, DeepFace and HaarCascade to detect faces in video streams and identify emotions by comparing faces to a training dataset. However, when testing the live emotion detection, the program crashed and was unable to handle changes in facial expressions in real-time as required for the intended demonstration. While the concepts and integration of libraries worked as expected on stored images, live video processing posed challenges that prevented a successful demonstration.
Fixing the program my computer learned: End-user debugging of machine-learned...City University London
This document summarizes Dr. Simone Stumpf's research into enabling end users to debug machine-learned programs. It discusses how machine-learned programs work and the challenges end users face in debugging programs they can't see the source code of. It describes formative studies exploring different explanation approaches and the types of feedback users provide. It also covers integrating user feedback to change the machine's reasoning, identifying unpredictable user-provided features, and directions for future work.
Chapter 10 Testing and Quality Assurance1Unders.docxketurahhazelhurst
Chapter 10:
Testing and Quality
Assurance
1
Understand quality & basic techniques for software verification and validation.
Analyze basics of software testing and testing techniques.
Discuss the concept of “inspection” process.
Objectives
2
Quality assurance (QA): activities designed
to measure and improve quality in a product— and process.
Quality control (QC): activities designed to validate and verify the quality of the product through detecting faults and “fixing” the defects.
Need good techniques, process, tools,
and team.
Testing Introduction
similar
3
Two traditional definitions:
Conforms to requirements.
Fit to use.
Verification: checking software conforms to
its requirements (did the software evolve
from the requirements properly; does the software “work”?)
Is the system correct?
Validation: checking software meets user requirements (fit to use)
Are we building the correct system?
What Is “Quality”?
4
Testing: executing program in a controlled environment and “verifying/validating” output.
Inspections and reviews.
Formal methods (proving software correct).
Static analysis detects “error-prone conditions.”
Some “Error-Detection” Techniques (finding errors)
5
Error: a mistake made by a programmer or software engineer that caused the fault, which in turn may cause a failure
Fault (defect, bug): condition that may cause a failure in the system
Failure (problem): inability of system to perform a function according to its spec due to some fault
Fault or failure/problem severity (based on consequences)
Fault or failure/problem priority (based on importance of developing a fix, which is in turn based
on severity)
Faults and Failures
6
Activity performed for:
Evaluating product quality
Improving products by identifying defects and having them fixed prior to software release
Dynamic (running-program) verification of program’s behavior on a finite set of test cases selected from execution domain.
Testing can NOT prove product works 100%—even though we use testing to demonstrate that parts of the software works.
Testing
Not always
done!
7
Who tests?
Programmers
Testers/Req. Analyst
Users
What is tested?
Unit code testing
Functional code testing
Integration/system testing
User interface testing
Testing (cont.)
Why test?
Acceptance (customer)
Conformance (std, laws, etc.)
Configuration (user vs. dev.)
Performance, stress, security, etc.
How (test cases designed)?
Intuition
Specification based (black box)
Code based (white box)
Existing cases (regression)
8
Progression of Testing
Equivalence Class Partitioning
Divide the input into several groups, deemed “equivalent” for purposes of finding errors.
Pick one “representative” for each class used for testing.
Equivalence classes determined by req./design specifications and some intuition
Example: pick “larger” of
two integers and . . .
Lessen duplication.
Complete coverage.
10
Suppose we have n distinct functional requirements.
Su ...
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
The document discusses empirical evaluation standards in software engineering research. It notes that evaluation standards have become more rigorous over time, as seen in a comparison of papers from 2001 and 2018. Today's evaluations generally analyze more data, make better use of inferential statistics, but still underutilize qualitative methods and rarely involve developers. The document emphasizes the importance of high quality data and validation when mining software repositories, as these sources often contain imprecise and incomplete information that can bias results. It also stresses selecting a diverse set of projects for studies to improve generalizability.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
Deep learning techniques can be used to learn features from data rather than relying on hand-crafted features. This allows neural networks to be applied to problems in computer vision, natural language processing, and other domains. Transfer learning techniques take advantage of features learned from one task and apply them to another related task, even when limited data is available for the second task. Deploying machine learning models in production requires techniques for serving predictions through scalable APIs and caching layers to meet performance requirements.
Machine Learning presentation. Helps you to have a brief idea about what machine learning is and gives you direction to go deep into it. It covers the idea of Supervised learning and unsupervised learning and examples of how to use different models.
Frequently asked tcs technical interview questions and answersnishajj
This document provides information about the interview process and sample technical questions for a job interview at TCS, an IT company. It describes the different types of interviews - campus interviews which are 45-60 minutes with one or more interviewers focusing on communication skills and background, and phone interviews which assess similar areas. It then provides examples of 20 technical questions that may be asked, covering topics like programming languages, data structures, OOP concepts, and more. Sample answers are provided for some of the questions.
The document discusses disassembly theory, including first, second, and third generation languages. It then discusses the why and how of disassembly, including uses for malware analysis, vulnerability analysis, software interoperability, compiler validation, and debugging displays. It describes the basic process of disassembly and two common algorithms: linear sweep and recursive descent. Finally, it outlines some common reversing and disassembly tools like file, PE Tools, PEiD, nm, ldd, objdump, otool, and strings.
Similar to Arabic Handwritten Script Recognition Towards Generalization: A Survey (20)
الجزء السادس ماذا ستقدم لعميلك ريادة الأعمال خطوة بخطوةRanda Elanwar
فى هذه السلسلة (السلسلة الثانية) نستكمل تقديم أساسيات علم ريادة الأعمال التجارية Business Entrepreneurship التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية MIT على منصة Edx بعنوان MITx: 15.390.2x Entrepreneurship 102: What can you do for your customer?
رابط الدورة: https://www.edx.org/course/entrepreneurship-102-what-can-you-do-mitx-15-390-2x
الجزء الرابع ماذا ستقدم لعميلك ريادة الأعمال خطوة بخطوةRanda Elanwar
فى هذه السلسلة (السلسلة الثانية) نستكمل تقديم أساسيات علم ريادة الأعمال التجارية Business Entrepreneurship التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية MIT على منصة Edx بعنوان MITx: 15.390.2x Entrepreneurship 102: What can you do for your customer?
رابط الدورة: https://www.edx.org/course/entrepreneurship-102-what-can-you-do-mitx-15-390-2x
الجزء الثاني ماذا ستقدم لعميلك ريادة الأعمال خطوة بخطوةRanda Elanwar
فى هذه السلسلة (السلسلة الثانية) نستكمل تقديم أساسيات علم ريادة الأعمال التجارية Business Entrepreneurship التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية MIT على منصة Edx بعنوان MITx: 15.390.2x Entrepreneurship 102: What can you do for your customer?
رابط الدورة: https://www.edx.org/course/entrepreneurship-102-what-can-you-do-mitx-15-390-2x
تدريب مدونة علماء مصر على الكتابة الفنية (الترجمة والتلخيص )_Pdf5of5Randa Elanwar
The document discusses translation, including what translation is, why we translate, and what is translated. It covers the different types of translation including literal, faithful, and free translation. It provides examples of fields that use translation such as diplomacy, industry, culture, science, history, economics, and politics. Translation is important for sharing knowledge and opening communication between peoples. The summary provides a high-level overview of the key topics and concepts discussed in the original Arabic document in 3 sentences.
تدريب مدونة علماء مصر على الكتابة الفنية (القصة القصيرة والخاطرة والأخطاء ال...Randa Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
تدريب مدونة علماء مصر على الكتابة الفنية (مقالات الموارد )_Pdf3of5Randa Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
تدريب مدونة علماء مصر على الكتابة الفنية (المقالات الإخبارية )_Pdf2of5Randa Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
تدريب مدونة علماء مصر على الكتابة الفنية (المقالات المبنية على البحث )_Pdf1of5Randa Elanwar
Egyptian scientists are developing a program called "Writing Skills" to help bloggers improve their writing abilities. The program covers various topics such as researching topics and sources for articles, structuring articles, citing sources, concluding articles, editing and reviewing articles, establishing the writer's point of view, and ending with a conclusion paragraph. Some key characteristics of a well-written article include being engaging, having a moderate length, clear language, an intriguing style, original ideas for readers, high-quality presentation of ideas, and supporting details and examples.
تعريف بمدونة علماء مصر ومحاور التدريب على الكتابة للمدونينRanda Elanwar
مرحبا بكم فى التدريب الأساسى لمدونى علماء مصر
التدريب الأساسى هو فقط مقدمة شاملة لتوسيع المدارك، وتصحيح المفاهيم الخاطئة، ولا يهدف إلى تدريب متخصص فى أى المحاور التى يتناولها
أولا: المقدمة وفيها تعريف بأبواب المدونة وأمثلة للمواضيع الفرعية التى يمكنك الكتابة فيها ومحاور التدريب
ثانيا: المحور الأول وفيه تدريب على هدف وهيكل المقالات المبنية على البحث واختيار الكلمات المفتاحية مع أمثلة
ثالثا: المحور الثانى وفيه تدريب على هدف وهيكل المقالات الإخبارية مع أمثلة
رابعا: المحور الثالث وفيه تدريب على هدف وهيكل مقالات الموارد مع أمثلة
خامسا: المحور الرابع وفيه تدريب على فنيات الكتابة للمقالات والقصة القصيرة والخاطرة وتلخيص للأخطاء اللغوية والإملائية الشائعة وعلامات الترقيم
سادسا المحور الخامس وفيه تدريب على كيفية الترجمة والتلخيص وأهم النصائح والأدوات
فى هذه السلسلة نقدم لك أساسيات علم ريادة الأعمال التجارية
Business Entrepreneurship
التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية
MIT
على منصة
Edx
بعنوان
MITx: 15.390.1x Entrepreneurship 101: Who is your customer?
رابط الدورة:
https://www.edx.org/course/entrepreneurship-101-who-customer-mitx-15-390-1x#.VL-MN0eUfHA
فى هذه السلسلة نقدم لك أساسيات علم ريادة الأعمال التجارية
Business Entrepreneurship
التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية
MIT
على منصة
Edx
بعنوان
MITx: 15.390.1x Entrepreneurship 101: Who is your customer?
رابط الدورة:
https://www.edx.org/course/entrepreneurship-101-who-customer-mitx-15-390-1x#.VL-MN0eUfHA
فى هذه السلسلة نقدم لك أساسيات علم ريادة الأعمال التجارية
Business Entrepreneurship
التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية
MIT
على منصة
Edx
بعنوان
MITx: 15.390.1x Entrepreneurship 101: Who is your customer?
رابط الدورة:
https://www.edx.org/course/entrepreneurship-101-who-customer-mitx-15-390-1x#.VL-MN0eUfHA
فى هذه السلسلة نقدم لك أساسيات علم ريادة الأعمال التجارية
Business Entrepreneurship
التى تحتاج أن تتعلمها قبل أن تقوم ببناء شركتك أو مؤسستك الهادفة للربح؛ حتى تتعرف على الخطوات الأولية للعمل وكيفية تنفيذها، وتكتشف المفاهيم الخاطئة السائدة، ثم تقوم فى النهاية ببناء تجارتك على أساس صحيح من وجهة نظر العميل، وليس من وجهة نظرك كصاحب العمل. هذه السلسلة هى ملخص للدروس المستفادة من دورة ريادة الأعمال المفتوحة التى يقدمها معهد ماساتشوستس للتقنية
MIT
على منصة
Edx
بعنوان
MITx: 15.390.1x Entrepreneurship 101: Who is your customer?
رابط الدورة:
https://www.edx.org/course/entrepreneurship-101-who-customer-mitx-15-390-1x#.VL-MN0eUfHA
هي قصة مشوار بدأ ولم ينتهِ بعد. سيكون فيها كل يوم شيءٌ جديد. سأتعلم وسأحكي لكم ما تعلمته. ربما وفّرت عليك التجربة لتُغيّرَ كثيرًا من قناعات لديك.
إن كنت طالبًا، أو حديث التخرج، وتنوي عمل دراسات عليا بمصر، فدعني أعرّفك قليلًا على أشياء خارج توقعاتك، إن لم يكن لديك فكرة. وإن كنت قد اتخذت خطواتك الأولى بالفعل فربما تجد في قصّتي ما يفسر ألغازك، ويهوّن عليك المفاجآت. لن أقول لك الآن ما مجال دراستي، فرغم احتمال أن تكون دارسًا لتخصصٍ آخر يختلف عني، ولكنني أثق أن لديك نفس الأسئلة، ونفس الشكوى
هي قصة مشوار بدأ ولم ينتهِ بعد. سيكون فيها كل يوم شيءٌ جديد. سأتعلم وسأحكي لكم ما تعلمته. ربما وفّرت عليك التجربة لتُغيّرَ كثيرًا من قناعات لديك.
إن كنت طالبًا، أو حديث التخرج، وتنوي عمل دراسات عليا بمصر، فدعني أعرّفك قليلًا على أشياء خارج توقعاتك، إن لم يكن لديك فكرة. وإن كنت قد اتخذت خطواتك الأولى بالفعل فربما تجد في قصّتي ما يفسر ألغازك، ويهوّن عليك المفاجآت. لن أقول لك الآن ما مجال دراستي، فرغم احتمال أن تكون دارسًا لتخصصٍ آخر يختلف عني، ولكنني أثق أن لديك نفس الأسئلة، ونفس الشكوى.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
Arabic Handwritten Script Recognition Towards Generalization: A Survey
1. 1
Arabic Handwritten ScriptArabic Handwritten Script
Recognition TowardsRecognition Towards
Generalization: A SurveyGeneralization: A SurveyAuthors:Authors:
Randa I. M. ElanwarRanda I. M. Elanwar
Assistant Researcher, Electronic Research Institute
Prof. Dr. Mohsen A. A. RashwanProf. Dr. Mohsen A. A. Rashwan
Professor of Digital Signal Processing, Electronic and communication dept, Cairo University
Prof. Dr. Samia A. A. MashaliProf. Dr. Samia A. A. Mashali
Head of computers and systems dept, Electronic Research Institute
2. 2
Presentation ContentsPresentation Contents
Introduction
Paper Objective
Arabic handwriting recognition problem
Main Challenges
Recent off-line Arabic handwriting recognition systems
Recent on-line Arabic handwriting recognition systems
Summary and Conclusion
3. 3
IntroductionIntroduction
Handwriting recognition can be defined as the task
of transforming text represented in the spatial form
of graphical marks into its symbolic representation
The main components of a recognizer are:
1. Capturing Data & acquisition
2. Preprocessing & segmentation
3. Defining patterns and model selection
4. Feature Extraction
5. Training
6. Classification
4. 4
IntroductionIntroduction
• First the input device captures an image and convert
it to a usable format
• Data is then preprocessed to eliminate noise for
simplification without loosing relevant information
and may also be segmented to smaller data units
5. 5
IntroductionIntroduction
• The information of each data unit is sent to feature
extractor to reduce them by measuring certain
“features” or “properties”
• Patterns (or classes) should be defined and models
should be selected. These models are trained using
the extracted features.
6. 6
IntroductionIntroduction
• The model for a pattern may be a single specific set
of features
• To recognize (or classify) a novel pattern means to
recover the model that generated the pattern based
on the extracted features
7. 7
IntroductionIntroduction
The feature extractor has reduced the data unit to a
point or feature vector X in a 2D feature space (or
observation space)
Classification rule: Classify the input as Class I if its
feature vector falls below the decision boundary shown,
and as Class II otherwise.
8. 8
IntroductionIntroduction
The problem is that designing a very complex
recognizer is unlikely to give good generalization since it
seems to be “tuned” to the particular training samples
The question is how to optimize this tradeoff:
generalization versus simple classifier
9. 9
IntroductionIntroduction
Usually there is an action taken based on the
classification decision. Each action should be assigned a
certain cost.
We design our decision boundary (classification rule)
so that on the average, the Risk will be as small as
possible.
The Risk (R) is the expected value of cost
Minimizing (R) leads to complex boundaries
The question is how to optimize this tradeoff:
generalization versus minimum risk?
10. 10
IntroductionIntroduction
In order to achieve general purpose recognizer
(unbiased) we should have a sufficient number of
training samples (N) for each class in the data set.
A theoretical estimate claims that
N ≅ 100 / P where P ≡ prob. of misclassification
I.e., for P ≈ 0.01, N ≈ 10000 and for P ≈ 0.03, N ≈ 3000
Such large data set (if available) needs large storage
and long processing time (time complexity)
The question is how to optimize this tradeoff:
generalization versus complexity?
11. 11
Paper ObjectivePaper Objective
Our concern in this paper is to:
1. provide a comprehensive review of recent off-line
and on-line trends in Arabic cursive handwriting
recognition (last 10 years publications)
2. clarify the challenges standing against obtaining a
reliable, accurate, simple, general purpose recognizer
based on these trends.
12. 12
Arabic Handwriting Recognition ProblemArabic Handwriting Recognition Problem
Arabic Script Recognition Systems are categorized as:
1. On-line or Off-line
2. Writer Dependent or Writer Independent
3. Open-vocabulary or closed-vocabulary
13. 13
Arabic Handwriting Recognition ProblemArabic Handwriting Recognition Problem
Types of Recognition:
When the input device is a digitizer tablet that
transmits the signal in real time or includes timing
information together with pen position, this is mostly
referred to as on-line or dynamic recognition
14. 14
Arabic Handwriting Recognition ProblemArabic Handwriting Recognition Problem
Types of Recognition:
When the input device is a still camera or a scanner,
which captures the position of digital ink on the page
but not the order in which it was laid down, this is
defined as off-line or image-based OCR
15. 15
Arabic Handwriting Recognition ProblemArabic Handwriting Recognition Problem
Special Characteristics of Arabic Script:
Always written from right to left
Arabic word consists of one or more portions; each
has one or more characters
Many characters differ only by the position and the
number of dots attached
16. 16
Arabic Handwriting Recognition ProblemArabic Handwriting Recognition Problem
Special Characteristics of Arabic Script:
Every character has more than one shape, depending
on its position
Characters overlap
17. 17
Arabic Handwriting Recognition ProblemArabic Handwriting Recognition Problem
Special Characteristics of Arabic Script:
Existence of ligatures
Due to having these special characteristics, Arabic
handwriting recognition systems still need more research
to be established commercially
18. 18
Main ChallengesMain Challenges
Feature Extraction
Noise
Model Selection and Complexity
Segmentation
Context
Evidence Pooling
Costs and Risks
Computational Complexity
Learning and Adaptation
19. 19
Main ChallengesMain Challenges
Feature Extraction:
A good feature set should helps distinguishing a class
from other classes, be invariant to differences and
contains no redundant information
20. 20
Main ChallengesMain Challenges
Feature Extraction:
A good feature set should helps distinguishing a class
from other classes, be invariant to differences and
contains no redundant information
… How to know which features are most
promising ?
… Is there ways to automatically learn which features are
best for a classifier?
21. 21
Main ChallengesMain Challenges
Feature Extraction:
A good feature set should helps distinguishing a class
from other classes, be invariant to differences and
contains no redundant information
… How to know which features are most
promising ?
… Is there ways to automatically learn which features are
best for a classifier?
It should be limited in number for computational ease
and to limit the amount of training data
22. 22
Main ChallengesMain Challenges
Feature Extraction:
A good feature set should helps distinguishing a class
from other classes, be invariant to differences and
contains no redundant information
… How to know which features are most
promising ?
… Is there ways to automatically learn which features are
best for a classifier?
It should be limited in number for computational ease
and to limit the amount of training data
… How many features
to use?
23. 23
Main ChallengesMain Challenges
Noise:
Random error in a pixel value (deformation) due to
signal-independent, signal-dependent and salt &
pepper noise.
Noise cannot always be totally eliminated; but
smoothing is done
24. 24
Main ChallengesMain Challenges
Noise:
Random error in a pixel value (deformation) due to
signal-independent, signal-dependent and salt &
pepper noise.
Noise cannot always be totally eliminated; but
smoothing is done
… Is the deformation in some signal is noise? or natural
varieties in true models?
… How can we use this information to improve
our classifier?
25. 25
Main ChallengesMain Challenges
Modeling Selection and Complexity:
Determining the complexity of the model: not so
simple that it cannot explain the differences between
the categories, yet not so complex as to give poor
classification on novel patterns.
26. 26
Main ChallengesMain Challenges
Modeling Selection and Complexity:
Determining the complexity of the model: not so
simple that it cannot explain the differences between
the categories, yet not so complex as to give poor
classification on novel patterns.
… how to know when to reject a class of models and
try another one?
… Are there principled methods for finding the best
complexity for a classifier?
… Is it a matter of random trial & error not even guided by
expectations of performance?
28. 28
Main ChallengesMain Challenges
Segmentation:
Segmentation subdivides image into its constituent
regions or objects. Segmentation should stop when the
objects of interest in an application have been isolated.
… How do we know where one character “ends” and the
next one “begin”?
… Shall we segment the images before they have been categorized or
categorize them
before they have been segmented?
29. 29
Main ChallengesMain Challenges
Context:
The accuracy of automatic handwriting recognition
systems based on purely visual information seems to
have a ceiling
Incorporating Symantec and syntactic knowledge
sources into the automatic recognition of text can offer
potential improvements in performance
… how, precisely, should we incorporate such
information?
30. 30
Main ChallengesMain Challenges
Evidence Pooling:
For high classification performance or for increased
class coverage, different classification tools are
developed either in parallel or sequentially
When having several component classifiers, and
these categorizers agree on a particular pattern, there
is no difficulty. But suppose they disagree !!!
31. 31
Main ChallengesMain Challenges
Evidence Pooling:
For high classification performance or for increased
class coverage, different classification tools are
developed either in parallel or sequentially
When having several component classifiers, and
these categorizers agree on a particular pattern, there
is no difficulty. But suppose they disagree !!!
… How should a “super” classifier pool the evidence from the component
recognizers to achieve the best decision?
… How would the “super” categorizer know when to base a decision on
a minority opinion when required?
32. 32
Main ChallengesMain Challenges
Costs and Risks:
A classifier is generally used to recommend actions,
each action having an associated cost or risk
We often design our classifier to recommend actions
that minimize some total expected cost or risk
33. 33
Main ChallengesMain Challenges
Costs and Risks:
A classifier is generally used to recommend actions,
each action having an associated cost or risk
We often design our classifier to recommend actions
that minimize some total expected cost or risk
… How do we incorporate knowledge about such risks and how will they
affect the classification decision?
… Is there a way to estimate the total risk and thus tell whether our
classifier is acceptable even before we field it?
34. 34
Main ChallengesMain Challenges
Computational Complexity:
Although we might achieve error-free recognition, the
time & storage requirements would be quite prohibitive
Some pattern recognition problems can be solved
using algorithms that are highly impractical.
35. 35
Main ChallengesMain Challenges
Computational Complexity:
Although we might achieve error-free recognition, the
time & storage requirements would be quite prohibitive
Some pattern recognition problems can be solved
using algorithms that are highly impractical.
… What is the tradeoff between computational ease
and performance?
… How can we optimize an excellent recognizer within the
engineering constraints ?
36. 36
Main ChallengesMain Challenges
Learning and Adaptation:
Any method that incorporates information from training
samples in the design of a classifier employs learning
If the models were extremely complicated, the classifier
would have complex decision boundaries
To overcome this, more training samples are needed to
obtain a better estimate of the true underlying features
In case of limited training samples, we should incorporate
knowledge of the problem domain. The production
representation is the “best” representation for classification.
37. 37
Main ChallengesMain Challenges
Learning and Adaptation:
Any method that incorporates information from training
samples in the design of a classifier employs learning
If the models were extremely complicated, the classifier
would have complex decision boundaries
To overcome this, more training samples are needed to
obtain a better estimate of the true underlying features
In case of limited training samples, we should incorporate
knowledge of the problem domain. The production
representation is the “best” representation for classification.
… How much training samples are needed for good generalization?
… How can we insure that the learning algorithm favors “simple”
solutions rather than complicated ones?
38. 38
Recent off-line Arabic handwriting recognitionRecent off-line Arabic handwriting recognition
systemssystems
Example: Pechwitz et al research [17]
proposed a recognition system based on a semi-continuous 1-D
HMM using the IFN/ENIT database of handwritten Tunisian
town/village names.
Preprocessing:
1. Extracting image contour and Performing a noise reduction filtering.
2. Skeletonization and normalization are performed.
3. Baseline estimation and word length normalization are performed.
39. 39
Recent off-line Arabic handwriting recognitionRecent off-line Arabic handwriting recognition
systemssystems
Example: Pechwitz et al research [17]
Feature Extraction:
1. A rectangular window is shifted from right to left across the
normalized gray level script image .
2. A Loeve-Karhunen Transformation is performed on the gray values
of each frame to reduce the number of features.
Modeling:
1. A HMM-model is generated for each character shape (all possible
positions) up to 160 different HMM-models.
2. Semi Continuous HMMs are used with 7 states per character.
40. 40
Recent off-line Arabic handwriting recognitionRecent off-line Arabic handwriting recognition
systemssystems
Example: Pechwitz et al research [17]
Database:
1. This database is split into four sets A, B, C & D.
2. The 4 sets contain 26,459 images of segmented Tunisian town
names (115,585 PAWs) handwritten by 411 unique writers.
3. 946 unique word labels, and 762 unique PAW labels.
4. For each image the ground truth information is available.
Lexicon:
The character shape HMM-models are combined to valid word
models using a tree structured lexicon with all 946 different
41. 41
Recent off-line Arabic handwriting recognitionRecent off-line Arabic handwriting recognition
systemssystems
Example: Pechwitz et al research [17]
Recognition:
The standard Viterbi Algorithm is used together with the lexicon.
The authors applied the recognition algorithm to the database
twice, once using the baseline coming from GT (ground truth) and
once using baseline they estimated.
Results:
Recognition rates 82 – 89% are obtained using baseline estimation
Recognition rates 89 – 95% are obtained using GT baseline
42. 42
Recent off-line Arabic handwriting recognitionRecent off-line Arabic handwriting recognition
systemssystems
Example: Pechwitz et al research [17]
Challenges:
1. Working on available database skips the limited training samples challenge
43. 43
Recent off-line Arabic handwriting recognitionRecent off-line Arabic handwriting recognition
systemssystems
Example: Pechwitz et al research [17]
Challenges:
1. Working on available database skips the limited training samples challenge
2. It is not easy to generalize this classifier for open vocabulary applications
because it works on a limited lexicon of words (segmentation-free
recognizer) otherwise context will be a must.
44. 44
Recent off-line Arabic handwriting recognitionRecent off-line Arabic handwriting recognition
systemssystems
Example: Pechwitz et al research [17]
Challenges:
1. Working on available database skips the limited training samples challenge
2. It is not easy to generalize this classifier for open vocabulary applications
because it works on a limited lexicon of words (segmentation-free
recognizer) otherwise context will be a must.
3. Generating the same HMM structure for all characters and ligatures i.e.,
modeling selection & complexity .. we think it would be much better to vary
the model structure according to each character requirement (ض shouldn’t
have the same model as ة for example).
45. 45
Recent off-line Arabic handwriting recognitionRecent off-line Arabic handwriting recognition
systemssystems
Example: Pechwitz et al research [17]
Challenges:
1. Working on available database skips the limited training samples challenge
2. It is not easy to generalize this classifier for open vocabulary applications
because it works on a limited lexicon of words (segmentation-free
recognizer) otherwise context will be a must.
3. Generating the same HMM structure for all characters and ligatures i.e.,
modeling selection & complexity .. we think it would be much better to vary
the model structure according to each character requirement (ض shouldn’t
have the same model as ة for example).
4. Feature Extraction: The idea of normalizing the word width to use a sliding
window feature extractor is pretty good except for the great dependency on
46. 46
Recent on-line Arabic handwriting recognitionRecent on-line Arabic handwriting recognition
systemssystems
Example: Biadsy et al research [24]
Preprocessing:
1. Geometrical processing phase to minimize handwriting variations.
2. A low-pass filter is used to reduce noise and remove imperfections
caused by acquisition devices.
3. The writing-speed is normalized by re-sampling the consequent
point sequences.
Feature Extraction:
Mainly angles (with x-axis) and loop-presence
47. 47
Recent on-line Arabic handwriting recognitionRecent on-line Arabic handwriting recognition
systemssystems
Example: Biadsy et al research [24]
Modeling:
1. The recognition framework uses discrete Left-to-right HMMs to
represent each Arabic letter shape (isolated, initial, medial, and
final).
2. The number of states for each letter shape model is based on the
geometric complexity of the letter shape. It varies from 5 to 11
states.
For example: 11 states are assigned to isolated ,ش and 5 states to
isolated .أ
48. 48
Recent on-line Arabic handwriting recognitionRecent on-line Arabic handwriting recognition
systemssystems
Example: Biadsy et al research [24]
Lexicon:
1. The Arabic dictionary D is subdivided into a set of sub-dictionaries {D1, D2,
…, Dn} based on the number of word parts in each word.
2. Letter-shape models are embedded in a network that represents a word-
part dictionary. The segmentation of word parts into letter-shapes and their
recognition are performed simultaneously in an integrated process.
D = {D = {انسان ،التحدى ،ثقافة ،جامعة ،رواية ،فادى ،محمد ،محمود ،معلم ،هل ،وسامانسان ،التحدى ،ثقافة ،جامعة ،رواية ،فادى ،محمد ،محمود ،معلم ،هل ،وسام}}
Sub-dictionaries of DSub-dictionaries of D Word-Part Dictionary for D3Word-Part Dictionary for D3
D1 = {D1 = {محمد ،معلم ،هلمحمد ،معلم ،هل}}
D2 = {D2 = {ثقافة ،جامعة ،محمودثقافة ،جامعة ،محمود}}
D3 = {D3 = {انسان ،التحدى ،فادى ،وسامانسان ،التحدى ،فادى ،وسام}}
D4 = {D4 = {روايةرواية}}
WPD3,1 = {WPD3,1 = {ا ،فا ،وا ،فا ،و}}
WPD3,2 = {WPD3,2 = {نسا ،لتحد ،د ،سانسا ،لتحد ،د ،سا}}
WPD3,3 = {WPD3,3 = {ن ،ى ،من ،ى ،م}}
49. 49
Recent on-line Arabic handwriting recognitionRecent on-line Arabic handwriting recognition
systemssystems
Example: Biadsy et al research [24]
Database:
1. 4 trainers are asked to write 800 selected words each.
2. For testing, 10 testers (the 4 trainers, in addition to 6 new volunteers) are
asked to write 280 words not in the training data (2,358 words in total).
3. 5 different dictionary sizes (5K, 10K, 20K, 30K, and 40K words) selected
from different Arabic websites are used. The 280 test words are present in
all dictionary sizes.
Recognition:
Writer dependent (WD) and writer independent (WI) experiments are done
and average word recognition rates 88 – 96% are obtained. The
50. 50
Recent on-line Arabic handwriting recognitionRecent on-line Arabic handwriting recognition
systemssystems
Example: Biadsy et al research [24]
Challenges:
1. Feature Extraction: The features they use are not enough to lead to
satisfying classification of general unconstrained handwritings.
Thus they are in a great need to work under limited vocabulary.
The word parts must be present in the dictionary or the will not be
recognized.
51. 51
Recent on-line Arabic handwriting recognitionRecent on-line Arabic handwriting recognition
systemssystems
Example: Biadsy et al research [24]
Challenges:
1. Feature Extraction: The features they use are not enough to lead to
satisfying classification of general unconstrained handwritings.
Thus they are in a great need to work under limited vocabulary.
The word parts must be present in the dictionary or the will not be
recognized.
2. Database they use looks unnatural. Volunteers are asked to follow
restrict methodology of writing which affects their individual writing
style. Besides, the system handles limited handwriting varieties
due to the small number of volunteers who wrote the database.
52. 52
Summary and ConclusionSummary and Conclusion
Foreign recognizers have found their way to the
markets as commercial products since years while
Arabic recognizers still need more time.
53. 53
Summary and ConclusionSummary and Conclusion
Foreign recognizers have found their way to the
markets as commercial products since years while
Arabic recognizers still need more time.
in the case of Arabic handwritten words many
researchers use a specific, more or less small data set
of their own ∴ it is impossible to compare different
results which would be important to improve existent
methods
54. 54
Summary and ConclusionSummary and Conclusion
Foreign recognizers have found their way to the
markets as commercial products since years while
Arabic recognizers still need more time.
in the case of Arabic handwritten words many
researchers use a specific, more or less small data set
of their own ∴ it is impossible to compare different
results which would be important to improve existent
methods
The complexity of the problem is greatly increased by
noise and by the infinite variability of handwritings
55. 55
Summary and ConclusionSummary and Conclusion
Cursive script requires the segmentation of words in
characters or parts of characters, i.e. graphemes, and
then the detection of individual features.
56. 56
Summary and ConclusionSummary and Conclusion
Cursive script requires the segmentation of words in
characters or parts of characters, i.e. graphemes, and
then the detection of individual features.
Generally, the holistic approach can be used if the
size of the vocabulary is small (such as the recognition
of the legal amount in cheques)
57. 57
Summary and ConclusionSummary and Conclusion
Cursive script requires the segmentation of words in
characters or parts of characters, i.e. graphemes, and
then the detection of individual features.
Generally, the holistic approach can be used if the
size of the vocabulary is small (such as the recognition
of the legal amount in cheques)
The character-based approach is the preferred
method for recognition applications that are
unconstrained or involve large-size vocabularies to
insure good generalization together with reasonable
complexity