Font Map is an interactive map of more than 750 fonts organized using machine learning to surface new relationships between fonts. For creative content services, please visit our website: https://www.artmiker.com
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
This document discusses offline handwritten Devanagari script recognition using a probabilistic neural network. It begins with an abstract that outlines the goal of recognizing offline handwritten Devanagari numerals using structural and local features classified with a probabilistic neural network classifier. The introduction provides background on handwritten numeral recognition challenges. The document then reviews related work on character recognition from the early 1900s to modern advancements, describes the Devanagari script, discusses theoretical neural network and proposed recognition methods, and concludes that accurate recognition depends on the input quality and more efficient, accurate systems are needed to recognize varied writing styles.
IRJET- Recognition of Handwritten Characters based on Deep Learning with Tens...IRJET Journal
This paper proposes a convolutional neural network model to recognize handwritten digits using the MNIST dataset. The model is built using TensorFlow and consists of convolutional, pooling and fully connected layers. The model is trained on 60,000 images and tested on 10,000 images, achieving 98% accuracy on the training set and classifying digits with low error of 0.03% on the test set. Previous methods for handwritten digit recognition are discussed and the CNN approach is shown to provide superior performance with faster training times compared to other models.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUESijcsitcejournal
Optical Character Recognition (OCR) is the process which enables a system to without human intervention
identifies the scripts or alphabets written into the users’ verbal communication. Optical Character
identification has grown to be individual of the mainly flourishing applications of knowledge in the field of
pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this
paper we resolve and examine the hypothetical and numerical models of Optical Character Identification.
The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR)
techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are
in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or
direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine
categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both
MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on
Optical Character detection or recognition has shown that the In Handwritten text there is no limitation
lying on the script technique. Hand written correspondence is complicated to be familiar through due to
diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of
approaches of Optical Character Identification is discussed here all along through their achievement.
This document provides an overview of an AI training session that includes:
1) An introduction to AI/ML concepts and use cases
2) Examples of AI tools like ChatGPT, DALL-E, Tome, Runway AI, and Midjourney
3) A roadmap for becoming a data scientist, data analyst, data engineer, ML engineer, or AIOps engineer
4) A hands-on session and bonus section to conclude the training
Handwritten Digit Recognition Using CNNIRJET Journal
This document discusses a research project on handwritten digit recognition using convolutional neural networks. The project aims to build a model that can recognize handwritten digits in images using the MNIST dataset to train a convolutional neural network. Specifically, it uses Keras and TensorFlow to create a 7-layer LeNet-5 CNN model on 70,000 MNIST images. The model is trained using stochastic gradient descent and backpropagation. Once trained, the model can be used to predict handwritten digits in new images. The document provides background on handwritten digit recognition and CNNs, describes the dataset and tools used, and outlines the methodology for building the recognition model.
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
This document discusses offline handwritten Devanagari script recognition using a probabilistic neural network. It begins with an abstract that outlines the goal of recognizing offline handwritten Devanagari numerals using structural and local features classified with a probabilistic neural network classifier. The introduction provides background on handwritten numeral recognition challenges. The document then reviews related work on character recognition from the early 1900s to modern advancements, describes the Devanagari script, discusses theoretical neural network and proposed recognition methods, and concludes that accurate recognition depends on the input quality and more efficient, accurate systems are needed to recognize varied writing styles.
IRJET- Recognition of Handwritten Characters based on Deep Learning with Tens...IRJET Journal
This paper proposes a convolutional neural network model to recognize handwritten digits using the MNIST dataset. The model is built using TensorFlow and consists of convolutional, pooling and fully connected layers. The model is trained on 60,000 images and tested on 10,000 images, achieving 98% accuracy on the training set and classifying digits with low error of 0.03% on the test set. Previous methods for handwritten digit recognition are discussed and the CNN approach is shown to provide superior performance with faster training times compared to other models.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUESijcsitcejournal
Optical Character Recognition (OCR) is the process which enables a system to without human intervention
identifies the scripts or alphabets written into the users’ verbal communication. Optical Character
identification has grown to be individual of the mainly flourishing applications of knowledge in the field of
pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this
paper we resolve and examine the hypothetical and numerical models of Optical Character Identification.
The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR)
techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are
in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or
direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine
categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both
MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on
Optical Character detection or recognition has shown that the In Handwritten text there is no limitation
lying on the script technique. Hand written correspondence is complicated to be familiar through due to
diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of
approaches of Optical Character Identification is discussed here all along through their achievement.
This document provides an overview of an AI training session that includes:
1) An introduction to AI/ML concepts and use cases
2) Examples of AI tools like ChatGPT, DALL-E, Tome, Runway AI, and Midjourney
3) A roadmap for becoming a data scientist, data analyst, data engineer, ML engineer, or AIOps engineer
4) A hands-on session and bonus section to conclude the training
Handwritten Digit Recognition Using CNNIRJET Journal
This document discusses a research project on handwritten digit recognition using convolutional neural networks. The project aims to build a model that can recognize handwritten digits in images using the MNIST dataset to train a convolutional neural network. Specifically, it uses Keras and TensorFlow to create a 7-layer LeNet-5 CNN model on 70,000 MNIST images. The model is trained using stochastic gradient descent and backpropagation. Once trained, the model can be used to predict handwritten digits in new images. The document provides background on handwritten digit recognition and CNNs, describes the dataset and tools used, and outlines the methodology for building the recognition model.
IRJET- Hand Sign Recognition using Convolutional Neural NetworkIRJET Journal
1) The document presents a study on using a convolutional neural network (CNN) to recognize American Sign Language (ASL) alphabets captured in real-time via a webcam.
2) The researchers trained a CNN model on 1600 images of 5 ASL alphabets (E, F, I, L, V) and tested it on 320 unlabeled images, achieving a validation accuracy of 74.8%.
3) While the model showed potential, the researchers acknowledged limitations like overfitting due to the small dataset and noted areas for improvement like recognizing a broader range of ASL letters and full sentences.
SP1: Exploratory Network Analysis with GephiJohn Breslin
ICWSM 2011 Tutorial
Sebastien Heymann and Julian Bilcke
Gephi is an interactive visualization and exploration software for all kinds of networks and relational data: online social networks, emails, communication and financial networks, but also semantic networks, inter-organizational networks and more. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections.
In this tutorial we will provide a hands-on demonstration of the essential functionalities of Gephi, based on a real case scenario: the exploration of student networks from the "Facebook100" dataset (Social Structure of Facebook Networks, Amanda L. Traud et al, 2011). The participants will be guided step by step through the complete chain of representation, manipulation, layout, analysis and aesthetics refinements. Particular focus will be put on filters and metrics for the creation of their first visualizations. They will be incited to compare the hypotheses suggested by their own exploration to the results actually published in the academic paper afterwards. They finally will walk away with the practical knowledge enabling them to use Gephi for their own projects. The tutorial is intended for professionals, researchers and graduates who wish to learn how playing during a network exploration can speed up their studies.
Sébastien Heymann is a Ph.D. Candidate in Computer Science at Université Pierre et Marie Curie, France. His research at the ComplexNetworks team focuses on the dynamics of realworld networks. He leads the Gephi project since 2008, and is the administrator of the Gephi Consortium.
Julian Bilcke is a Software Engineer at ISC-PIF (Complex Systems Institute of Paris, France). He is a founder and a developer for the Gephi project since 2008.
Abstract: The main communication methods used by deaf people are sign language, but opposed to common thought, there is no specific universal sign language: every country or even regional group uses its own set of signs. The use of sign language in digital systems can enhance communication in both directions: animated avatars can synthesize signals based on voice or text recognition; and sign language can be translated into various text or sound forms based on different images, videos and sensors input. The ultimate goal of this research, but it is not a simple spelling of spoken language, so that recognizing different signs or letters of the alphabet (which has been a common approach) is not sufficient for its transcription and automatic interpretation. Here proposes an algorithm and method for an application this would help us in recognising the various user defined signs. The palm images of right and left hand are loaded at runtime. Firstly these images will be seized and stored in directory. Then technique called Template matching is used for finding areas of an image that match (are similar) to a template image (patch). Our goal is to detect the highest matching area. We need two primary components- A) Source image (I): In the template image in which we try to find a match. B) Template image (T): The patch image which will be compared to the template image. In proposed system user defined patterns will be having 60% accuracy while default patterns will be provided with 80% accuracy.
This 3-sentence summary provides the high-level information about the ICWSM'11 tutorial document:
The tutorial document announces a workshop on exploratory network analysis using Gephi, an open-source graph visualization and manipulation software, to be held on July 17, 2011 from 1-4 PM with instructors Sébastien Heymann and Julian Bilcke. The tutorial will provide an introduction to Gephi and guide participants through importing data, network visualization and manipulation, analysis, and aesthetics refinements using real datasets. Participants will work in teams and present preliminary results with the goal of learning practical skills for using Gephi on their own projects.
This document describes a project to develop a hand gesture detection model using computer vision and machine learning. The model aims to recognize Indian sign language gestures from video input and output the corresponding text. The team has made progress training models to recognize alphabets with 80% accuracy and common phrases like "Hello" and "Welcome" with 85% accuracy. The final outcome will be a working gesture detection system to help communication for deaf or mute users.
Character recognition for bi lingual mixed-type characters using artificial n...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Makine Öğrenmesi ile Görüntü Tanıma | Image Recognition using Machine LearningAli Alkan
The document provides an introduction to image processing and recognition using machine learning. It discusses how deep learning uses hierarchical neural networks inspired by the human brain to learn representations of image data without requiring manual feature engineering. Deep learning has been applied successfully to problems like computer vision through convolutional neural networks. The document also describes how KNIME can be used as an open-source platform to visually build and run deep learning models for image processing tasks and integrate with other tools. It highlights several image processing and deep learning nodes available in KNIME.
Generative AI: A Comprehensive Tech Stack BreakdownBenjaminlapid1
Build a reliable and effective generative AI system with the right generative AI tech stack that helps create smarter solutions and drive growth.
Click here for more information: https://www.leewayhertz.com/generative-ai-tech-stack/
Handwritten Text Recognition and Digital Text Conversionijtsrd
Sometimes it is extremely difficult to secure handwritten documents in the real world. While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc. So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems. The main aim of our application is to recognize hand written text and display it in digital text format. Image processing is very significant process for data analysis these days. In image processing, the visible text from the real world as input must be processed precisely in order to produce the same information as output with accuracy. To do this, the text present in the image must be recognized by the system accurately. The proposed system aims at achieving these results. The process goes in this way The image which contains the handwritten text is fed to the system is passed into neural network which recognizes the handwritten text present in the image and displays it in the form of digital text. This can be used for many purposes such as copying the digital text for using it elsewhere, producing formal documents and can also be used as input for data processing. Using this process, we can store the information in a secure way, we can access the information from anywhere or at any time and there is no scope for physical damage as the information is in digital format. Mr. B. Ravinder Reddy | J. Nandini | P. Sowmya | Y. Sathwik ""Handwritten Text Recognition and Digital Text Conversion"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23508.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23508/handwritten-text-recognition-and-digital-text-conversion/mr-b-ravinder-reddy
This document summarizes a presentation on deep image processing and computer vision. It introduces common deep learning techniques like CNNs, autoencoders, variational autoencoders and generative adversarial networks. It then discusses applications including image classification using models like LeNet, AlexNet and VGG. It also covers face detection, segmentation, object detection algorithms like R-CNN, Fast R-CNN and Faster R-CNN. Additional topics include document automation using character recognition and graphical element analysis, as well as identity recognition using face detection. Real-world examples are provided for document processing, handwritten letter recognition and event pass verification.
This document discusses several potential artificial intelligence projects from students at HKBK College of Engineering. It describes projects to develop a creative AI using deep learning to generate art, music and stories. Another project aims to use time series analysis and natural language processing to predict stock performance. A third project discusses using deep learning models to detect diseases from medical scans to improve healthcare.
Character Recognition (Devanagari Script)IJERA Editor
This document summarizes research on using neural networks for optical character recognition of Devanagari script characters. It describes preprocessing scanned images, extracting features using neural networks, and post-processing to recognize characters. The system was tested on a dataset of Devanagari characters with neural networks trained over multiple epochs. Recognition accuracy increased with larger training sets as the network learned to identify characters more precisely. The system demonstrates an effective approach for digitally recognizing handwritten Devanagari characters.
A real time facial emotion recognition using 3D sensor and interfacing the re...Mounika Kakarla
This document discusses a system for recognizing facial emotions in real-time using a Kinect depth sensor and interfacing those emotions with a 3D virtual avatar in Second Life. The system analyzes facial feature points using the Kinect to detect emotions like smile, surprise, fear, anger and sad. It then transfers the detected emotions to an avatar in Second Life in real-time. The goal is to help speech-impaired people communicate emotions through an avatar. The system was implemented in two phases - facial emotion recognition using Kinect, and displaying linked emotions on an avatar in Second Life.
HOW CONVOLUTIONAL NEURAL NETWORKS WORK_.pptxWriteMe
Convolutional neural networks are a type of artificial neural network useful for image recognition. Multiple layers stack up to make ConvNets. Each layer contains a number of neurons. The first layer is the input layer and the last layer is the output layer. Originally published at https://writeme.ai/blog/how-convolutional-neural-networks-work/#final-output-of-convolutional-neural-networks
This document summarizes a research paper that evaluated different machine learning algorithms for offline handwritten digit recognition. The researchers tested Multilayer Perceptron, Support Vector Machine, Naive Bayes, Bayes Net, Random Forest, J48 and Random Tree classifiers using the WEKA machine learning toolkit. The Multilayer Perceptron achieved the highest accuracy of 90.37% for recognizing handwritten digits. The paper aims to develop effective approaches for handwritten digit recognition using machine learning techniques.
The document describes a project to develop a real-time sign language detection system using computer vision and deep learning techniques. The researchers collected over 500 images of 5 different signs and trained a convolutional neural network model using transfer learning with a pre-trained SSD MobileNet V2 model. The model takes input from a webcam video stream and classifies each frame in real-time to detect the sign language. Some key applications of this system include improving communication for deaf individuals and teaching sign language. The researchers achieved reliable detection results under controlled lighting conditions and aim to expand the dataset and model capabilities in future work.
This document provides an overview of building a Persian handwritten digit recognition model. It introduces machine learning concepts like supervised and unsupervised learning. It discusses TensorFlow and the MNIST dataset. It demonstrates how to build a basic MNIST model in Python with TensorFlow. It also shows how to create an Android app to detect handwritten digits using a TensorFlow model. Finally, it proposes using Custom Vision AI to create a Persian MNIST dataset and train a model to recognize Persian handwritten digits.
The document discusses artificial intelligence, including its history, applications, and languages. It provides an overview of AI, noting that it aims to recreate human intelligence through machine learning and problem solving. The document then covers key topics like the philosophy of AI, limits on machine intelligence, and comparisons between human and artificial brains. It also gives brief histories of AI and machine learning. The document concludes by discussing popular AI programming languages like Lisp and Prolog, as well as various applications of AI technologies.
HOW CONVOLUTIONAL NEURAL NETWORKS WORK_ (1).pptxWriteMe
Convolutional neural networks are a type of artificial neural network useful for image recognition. Multiple layers stack up to make ConvNets. Each layer contains a number of neurons. The first layer is the input layer and the last layer is the output layer. See more.....https://writeme.ai/blog/how-convolutional-neural-networks-work/#convolutional-filters-for-image-processing
Canva, a popular document editing and template site, has created a tool that both students and teachers may utilize. Canva for Education, as it is known, is a free feature available to K-12 teachers and their students. For creative content services, please visit our website: https://www.artmiker.com
Fontjoy is a font generator that helps the user to easily create a font pairing for a design project. For creative content services, please visit our website: https://www.artmiker.com
IRJET- Hand Sign Recognition using Convolutional Neural NetworkIRJET Journal
1) The document presents a study on using a convolutional neural network (CNN) to recognize American Sign Language (ASL) alphabets captured in real-time via a webcam.
2) The researchers trained a CNN model on 1600 images of 5 ASL alphabets (E, F, I, L, V) and tested it on 320 unlabeled images, achieving a validation accuracy of 74.8%.
3) While the model showed potential, the researchers acknowledged limitations like overfitting due to the small dataset and noted areas for improvement like recognizing a broader range of ASL letters and full sentences.
SP1: Exploratory Network Analysis with GephiJohn Breslin
ICWSM 2011 Tutorial
Sebastien Heymann and Julian Bilcke
Gephi is an interactive visualization and exploration software for all kinds of networks and relational data: online social networks, emails, communication and financial networks, but also semantic networks, inter-organizational networks and more. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections.
In this tutorial we will provide a hands-on demonstration of the essential functionalities of Gephi, based on a real case scenario: the exploration of student networks from the "Facebook100" dataset (Social Structure of Facebook Networks, Amanda L. Traud et al, 2011). The participants will be guided step by step through the complete chain of representation, manipulation, layout, analysis and aesthetics refinements. Particular focus will be put on filters and metrics for the creation of their first visualizations. They will be incited to compare the hypotheses suggested by their own exploration to the results actually published in the academic paper afterwards. They finally will walk away with the practical knowledge enabling them to use Gephi for their own projects. The tutorial is intended for professionals, researchers and graduates who wish to learn how playing during a network exploration can speed up their studies.
Sébastien Heymann is a Ph.D. Candidate in Computer Science at Université Pierre et Marie Curie, France. His research at the ComplexNetworks team focuses on the dynamics of realworld networks. He leads the Gephi project since 2008, and is the administrator of the Gephi Consortium.
Julian Bilcke is a Software Engineer at ISC-PIF (Complex Systems Institute of Paris, France). He is a founder and a developer for the Gephi project since 2008.
Abstract: The main communication methods used by deaf people are sign language, but opposed to common thought, there is no specific universal sign language: every country or even regional group uses its own set of signs. The use of sign language in digital systems can enhance communication in both directions: animated avatars can synthesize signals based on voice or text recognition; and sign language can be translated into various text or sound forms based on different images, videos and sensors input. The ultimate goal of this research, but it is not a simple spelling of spoken language, so that recognizing different signs or letters of the alphabet (which has been a common approach) is not sufficient for its transcription and automatic interpretation. Here proposes an algorithm and method for an application this would help us in recognising the various user defined signs. The palm images of right and left hand are loaded at runtime. Firstly these images will be seized and stored in directory. Then technique called Template matching is used for finding areas of an image that match (are similar) to a template image (patch). Our goal is to detect the highest matching area. We need two primary components- A) Source image (I): In the template image in which we try to find a match. B) Template image (T): The patch image which will be compared to the template image. In proposed system user defined patterns will be having 60% accuracy while default patterns will be provided with 80% accuracy.
This 3-sentence summary provides the high-level information about the ICWSM'11 tutorial document:
The tutorial document announces a workshop on exploratory network analysis using Gephi, an open-source graph visualization and manipulation software, to be held on July 17, 2011 from 1-4 PM with instructors Sébastien Heymann and Julian Bilcke. The tutorial will provide an introduction to Gephi and guide participants through importing data, network visualization and manipulation, analysis, and aesthetics refinements using real datasets. Participants will work in teams and present preliminary results with the goal of learning practical skills for using Gephi on their own projects.
This document describes a project to develop a hand gesture detection model using computer vision and machine learning. The model aims to recognize Indian sign language gestures from video input and output the corresponding text. The team has made progress training models to recognize alphabets with 80% accuracy and common phrases like "Hello" and "Welcome" with 85% accuracy. The final outcome will be a working gesture detection system to help communication for deaf or mute users.
Character recognition for bi lingual mixed-type characters using artificial n...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Makine Öğrenmesi ile Görüntü Tanıma | Image Recognition using Machine LearningAli Alkan
The document provides an introduction to image processing and recognition using machine learning. It discusses how deep learning uses hierarchical neural networks inspired by the human brain to learn representations of image data without requiring manual feature engineering. Deep learning has been applied successfully to problems like computer vision through convolutional neural networks. The document also describes how KNIME can be used as an open-source platform to visually build and run deep learning models for image processing tasks and integrate with other tools. It highlights several image processing and deep learning nodes available in KNIME.
Generative AI: A Comprehensive Tech Stack BreakdownBenjaminlapid1
Build a reliable and effective generative AI system with the right generative AI tech stack that helps create smarter solutions and drive growth.
Click here for more information: https://www.leewayhertz.com/generative-ai-tech-stack/
Handwritten Text Recognition and Digital Text Conversionijtsrd
Sometimes it is extremely difficult to secure handwritten documents in the real world. While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc. So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems. The main aim of our application is to recognize hand written text and display it in digital text format. Image processing is very significant process for data analysis these days. In image processing, the visible text from the real world as input must be processed precisely in order to produce the same information as output with accuracy. To do this, the text present in the image must be recognized by the system accurately. The proposed system aims at achieving these results. The process goes in this way The image which contains the handwritten text is fed to the system is passed into neural network which recognizes the handwritten text present in the image and displays it in the form of digital text. This can be used for many purposes such as copying the digital text for using it elsewhere, producing formal documents and can also be used as input for data processing. Using this process, we can store the information in a secure way, we can access the information from anywhere or at any time and there is no scope for physical damage as the information is in digital format. Mr. B. Ravinder Reddy | J. Nandini | P. Sowmya | Y. Sathwik ""Handwritten Text Recognition and Digital Text Conversion"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23508.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23508/handwritten-text-recognition-and-digital-text-conversion/mr-b-ravinder-reddy
This document summarizes a presentation on deep image processing and computer vision. It introduces common deep learning techniques like CNNs, autoencoders, variational autoencoders and generative adversarial networks. It then discusses applications including image classification using models like LeNet, AlexNet and VGG. It also covers face detection, segmentation, object detection algorithms like R-CNN, Fast R-CNN and Faster R-CNN. Additional topics include document automation using character recognition and graphical element analysis, as well as identity recognition using face detection. Real-world examples are provided for document processing, handwritten letter recognition and event pass verification.
This document discusses several potential artificial intelligence projects from students at HKBK College of Engineering. It describes projects to develop a creative AI using deep learning to generate art, music and stories. Another project aims to use time series analysis and natural language processing to predict stock performance. A third project discusses using deep learning models to detect diseases from medical scans to improve healthcare.
Character Recognition (Devanagari Script)IJERA Editor
This document summarizes research on using neural networks for optical character recognition of Devanagari script characters. It describes preprocessing scanned images, extracting features using neural networks, and post-processing to recognize characters. The system was tested on a dataset of Devanagari characters with neural networks trained over multiple epochs. Recognition accuracy increased with larger training sets as the network learned to identify characters more precisely. The system demonstrates an effective approach for digitally recognizing handwritten Devanagari characters.
A real time facial emotion recognition using 3D sensor and interfacing the re...Mounika Kakarla
This document discusses a system for recognizing facial emotions in real-time using a Kinect depth sensor and interfacing those emotions with a 3D virtual avatar in Second Life. The system analyzes facial feature points using the Kinect to detect emotions like smile, surprise, fear, anger and sad. It then transfers the detected emotions to an avatar in Second Life in real-time. The goal is to help speech-impaired people communicate emotions through an avatar. The system was implemented in two phases - facial emotion recognition using Kinect, and displaying linked emotions on an avatar in Second Life.
HOW CONVOLUTIONAL NEURAL NETWORKS WORK_.pptxWriteMe
Convolutional neural networks are a type of artificial neural network useful for image recognition. Multiple layers stack up to make ConvNets. Each layer contains a number of neurons. The first layer is the input layer and the last layer is the output layer. Originally published at https://writeme.ai/blog/how-convolutional-neural-networks-work/#final-output-of-convolutional-neural-networks
This document summarizes a research paper that evaluated different machine learning algorithms for offline handwritten digit recognition. The researchers tested Multilayer Perceptron, Support Vector Machine, Naive Bayes, Bayes Net, Random Forest, J48 and Random Tree classifiers using the WEKA machine learning toolkit. The Multilayer Perceptron achieved the highest accuracy of 90.37% for recognizing handwritten digits. The paper aims to develop effective approaches for handwritten digit recognition using machine learning techniques.
The document describes a project to develop a real-time sign language detection system using computer vision and deep learning techniques. The researchers collected over 500 images of 5 different signs and trained a convolutional neural network model using transfer learning with a pre-trained SSD MobileNet V2 model. The model takes input from a webcam video stream and classifies each frame in real-time to detect the sign language. Some key applications of this system include improving communication for deaf individuals and teaching sign language. The researchers achieved reliable detection results under controlled lighting conditions and aim to expand the dataset and model capabilities in future work.
This document provides an overview of building a Persian handwritten digit recognition model. It introduces machine learning concepts like supervised and unsupervised learning. It discusses TensorFlow and the MNIST dataset. It demonstrates how to build a basic MNIST model in Python with TensorFlow. It also shows how to create an Android app to detect handwritten digits using a TensorFlow model. Finally, it proposes using Custom Vision AI to create a Persian MNIST dataset and train a model to recognize Persian handwritten digits.
The document discusses artificial intelligence, including its history, applications, and languages. It provides an overview of AI, noting that it aims to recreate human intelligence through machine learning and problem solving. The document then covers key topics like the philosophy of AI, limits on machine intelligence, and comparisons between human and artificial brains. It also gives brief histories of AI and machine learning. The document concludes by discussing popular AI programming languages like Lisp and Prolog, as well as various applications of AI technologies.
HOW CONVOLUTIONAL NEURAL NETWORKS WORK_ (1).pptxWriteMe
Convolutional neural networks are a type of artificial neural network useful for image recognition. Multiple layers stack up to make ConvNets. Each layer contains a number of neurons. The first layer is the input layer and the last layer is the output layer. See more.....https://writeme.ai/blog/how-convolutional-neural-networks-work/#convolutional-filters-for-image-processing
Canva, a popular document editing and template site, has created a tool that both students and teachers may utilize. Canva for Education, as it is known, is a free feature available to K-12 teachers and their students. For creative content services, please visit our website: https://www.artmiker.com
Fontjoy is a font generator that helps the user to easily create a font pairing for a design project. For creative content services, please visit our website: https://www.artmiker.com
Charisma AI is a bot mainly used for storytelling that has built in features like emotion, memory, scenes and subplots. For creative content services, please visit our website: https://www.artmiker.com
Pear Deck elevates slide-based presentations to a new level of interaction and engagement. For creative content services, please visit our website: https://www.artmiker.com
Rive is a cutting-edge tool for producing high-performance interactive animations that can be run anywhere.
For creative content services, please visit our website: https://www.artmiker.com
Soundbible provide free and royalty-free sound effects and clips for use by video editors, film composers, game creators, and weekend sound enthusiasts.
For creative content services, please visit our website: https://www.artmiker.com
This deck contains information about Skylab, an AI photo and video processing service that makes high quality retouching accessible to anyone in the world.
For creative content services, please visit our website: https://www.artmiker.com
This deck contains information about Workplace, a communication tool that connects everyone in your company, even if they’re working remotely.
For creative content services, please visit our website: https://www.artmiker.com
This deck contains basic information about Hypefury, a content posting and scheduling tool for Twitter.
For creative content services, please visit our website: https://www.artmiker.com
This deck contains basic information about Repository Drives, one of the most vital requirements in backing up & storing files, whether mobile, desktop, or any other platforms.
For creative content services, please visit our website: https://www.artmiker.com
This deck is composed of the basic information about Kaedim, an AI-based tool that can turn 2D images, sketches, and even art pieces generated by other AIs into 3D models with good topology.
For creative content services, please visit our website: https://www.artmiker.com
This deck is composed of the basic information about Flowframes, a simple but powerful app that utilizes advanced AI frameworks to interpolate videos in order to increase their framerate in the most natural looking way possible.
For creative content services, please visit our website: https://www.artmiker.com
This deck is composed of the basic information about Artgrid, a platform that offers streamlined, high-quality footage licensing for filmmakers and video creators.
For creative content services, please visit our website: https://www.artmiker.com
Notion is a software that allows users to create documents, databases, websites, and project management systems. It aims to improve productivity and efficiency for teams by allowing members to manage deadlines, goals, and tasks in one shared workspace. Key features include connecting teams and projects in one place, customizing workflows, and accessing community resources and support. The software can be used for work or personal purposes by choosing templates and modifying pages using formatting tools and block types. Pricing plans are available for individual and team use.
A simplified character rigging & animation tutorial using the Spine animation tool. This is so easy to learn and use.
For creative content services, please visit our website:
https://www.artmiker.com
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
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Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
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Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
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* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
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Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
1. FONT MAP
• artmiker •
Produced by Artmiker Studios on: July 1, 2023. All Intellectual Property mentioned in this document are owned by their own respective owners. All Rights Reserved.
Organizing the World of Fonts with AI
2. Fonts are more than just a collection of letters; they are an expression
of style, mood, and personality.
Fonts are the visual embodiment of written communication,
influencing the way we perceive and interpret text.
From elegant and sophisticated scripts to bold and assertive sans-serifs,
fonts have the power to convey emotions, establish brand identities,
and enhance the overall reading experience.
Introduction
3. What is FONT MAP?
● Font Map is an interactive map of more than 750 fonts organized
using machine learning to surface new relationships between fonts.
● Developer – Kevin Ho
● Support - Tobias Toft, Jochen Maria Weber, and the design
community at IDEO.
● Program used – Tensorflow and D3.js
source: experiments.withgoogle
4. AI in Organizing Visual Information
● Andrej Karpathy’s AI organized photos
● Thousands of photos were organized
by AI into a single map through higher
order visual recognition.
● This shows the effectivity of AI in
organizing visual information.
source: ideo stories
5. Exploring Fonts
● A diagram showing the
flow of the machine
learning algorithm from
font samples using the
word “handgloves” to the
assigned point in 2d space.
● Type designers often use
the term “handgloves” to
examine fonts.
source: ideo stories
6. AI Experiment
● Creation of training set of images, one for each font, using the
word “handgloves” for algorithm.
● This allowed each image to contain enough characters to represent
the various traits of each font.
● After having a bunch of font images, convolutional neural network
called VGG16 was used to generate for each font a list of numbers
that represents what the network thought were the notable visual
features of the image.
source: ideo stories
7. AI Experiment
● Neural Networks - are a subset of machine learning, and they are at
the heart of deep learning algorithms.
● Convolutional Neural Network (CNN) - Provide a more scalable
approach to image classification and object recognition tasks,
leveraging principles from linear algebra, specifically matrix
multiplication, to identify patterns within an image.
source: ideo stories
8. AI Experiment
● To get x,y points, embeddings
were ran through T-SNE.
● T-SNE is a popular algorithm
for taking large vectors and
compressing them into a
smaller space — in this case
into a 2-D plane.
source: ideo stories
9. AI Experiment
● The process resulted with 800
fonts in a 2-D space.
● There were clear font clusters
like a sans-serif group, and a
group of cursive fonts.
● The algorithm was also able
to cluster outliers, or fonts
that were more unique and
had fewer relations with
others.
source: ideo stories
10. Introducing Font Map
● The final result of
this exploration is
Font Map, an
interactive map of
more than 750
fonts organized
using machine
learning.
● An intelligent
systems that can
aid the design
process.
source: ideo stories
11. Citations:
Organizing the World of Fonts with AI. (Apr 20, 2017). Retrieved May 09, 2023, from
https://medium.com/ideo-stories/organizing-the-world-of-fonts-with-ai-7d9e49ff2b25
Font Map. (July, 2017). Retrieved May 08, 2023, from https://experiments.withgoogle.com/font-map
Convolutional Neural Networks. (n.d.). Retrieved May 08, 2023, from https://www.ibm.com/topics/convolutional-neural-networks