An artificial neural network approach to optical character recognition (OCR) is presented using a multi-layer perceptron model. The model acquires an image, preprocesses it through steps like grayscale conversion and segmentation, extracts features by mapping characters to matrices, then trains a neural network to classify characters. Experimental results show 91.53% accuracy for isolated characters and 80.65% for characters in sentences.
A Review of Optical Character Recognition System for Recognition of Printed Textiosrjce
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.
Optical Character Recognition from Text ImageEditor IJCATR
Optical Character Recognition (OCR) is a system that provides a full alphanumeric recognition of printed or handwritten
characters by simply scanning the text image. OCR system interprets the printed or handwritten characters image and converts it into
corresponding editable text document. The text image is divided into regions by isolating each line, then individual characters with
spaces. After character extraction, the texture and topological features like corner points, features of different regions, ratio of
character area and convex area of all characters of text image are calculated. Previously features of each uppercase and lowercase
letter, digit, and symbols are stored as a template. Based on the texture and topological features, the system recognizes the exact
character using feature matching between the extracted character and the template of all characters as a measure of similarity.
CHARACTER RECOGNITION USING NEURAL NETWORK WITHOUT FEATURE EXTRACTION FOR KAN...Editor IJMTER
Handwriting recognition has been one of the active and challenging research areas in the
field of pattern recognition. It has numerous applications which include, reading aid for blind, bank
cheques and conversion of any hand written document into structural text form[1]. As there are no
sufficient number of works on Indian language character recognition especially Kannada script
among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten
Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized
into 60x40 pixel.The resized character is used for training the neural network. Once the training
process is completed the same character is given as input to the neural network with different set of
neurons in hidden layer and their recognition accuracy rate for different kannada characters has been
calculated and compared. The results show that the proposed system yields good recognition
accuracy rates comparable to that of other handwritten character recognition systems.
OCR application is developed with IMAQ Vision for LabVIEW software- developing tool and it uses a commercial digital camera from any android phone as image acquisition device.The proposed device will assist visually handicapped people in reading the printed text matter in English Language. It will look out for text signs which may be written text on printed books, newspapers, posters, etc and then live video frames of the text will be sent to labview for image processing and finally the recognised characters will be spoken.
A Review of Optical Character Recognition System for Recognition of Printed Textiosrjce
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.
Optical Character Recognition from Text ImageEditor IJCATR
Optical Character Recognition (OCR) is a system that provides a full alphanumeric recognition of printed or handwritten
characters by simply scanning the text image. OCR system interprets the printed or handwritten characters image and converts it into
corresponding editable text document. The text image is divided into regions by isolating each line, then individual characters with
spaces. After character extraction, the texture and topological features like corner points, features of different regions, ratio of
character area and convex area of all characters of text image are calculated. Previously features of each uppercase and lowercase
letter, digit, and symbols are stored as a template. Based on the texture and topological features, the system recognizes the exact
character using feature matching between the extracted character and the template of all characters as a measure of similarity.
CHARACTER RECOGNITION USING NEURAL NETWORK WITHOUT FEATURE EXTRACTION FOR KAN...Editor IJMTER
Handwriting recognition has been one of the active and challenging research areas in the
field of pattern recognition. It has numerous applications which include, reading aid for blind, bank
cheques and conversion of any hand written document into structural text form[1]. As there are no
sufficient number of works on Indian language character recognition especially Kannada script
among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten
Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized
into 60x40 pixel.The resized character is used for training the neural network. Once the training
process is completed the same character is given as input to the neural network with different set of
neurons in hidden layer and their recognition accuracy rate for different kannada characters has been
calculated and compared. The results show that the proposed system yields good recognition
accuracy rates comparable to that of other handwritten character recognition systems.
OCR application is developed with IMAQ Vision for LabVIEW software- developing tool and it uses a commercial digital camera from any android phone as image acquisition device.The proposed device will assist visually handicapped people in reading the printed text matter in English Language. It will look out for text signs which may be written text on printed books, newspapers, posters, etc and then live video frames of the text will be sent to labview for image processing and finally the recognised characters will be spoken.
If your organization is considering or has deployed optical character recognition software for your in-house utility bill data processing needs, you may be doing more work than you need to.
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
Optical Character Recognition Using PythonYogeshIJTSRD
Optical Character Recognition is a process of classifying optical patterns with respect to alphanumeric or other characters. It also includes segmentation, feature extraction and classification. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with. representation learning The idea of the project is to extract text from image using Deep Learning by OCR Ponvizhi. U | Ramya. P | Ramya. R "Optical Character Recognition Using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41099.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/41099/optical-character-recognition-using-python/ponvizhi-u
Text Extraction is a process by which we convert Printed document/Scanned Page or Image in which text are available to ASCII Character that a Computer can Recognize.
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.
If your organization is considering or has deployed optical character recognition software for your in-house utility bill data processing needs, you may be doing more work than you need to.
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
Optical Character Recognition Using PythonYogeshIJTSRD
Optical Character Recognition is a process of classifying optical patterns with respect to alphanumeric or other characters. It also includes segmentation, feature extraction and classification. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with. representation learning The idea of the project is to extract text from image using Deep Learning by OCR Ponvizhi. U | Ramya. P | Ramya. R "Optical Character Recognition Using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41099.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/41099/optical-character-recognition-using-python/ponvizhi-u
Text Extraction is a process by which we convert Printed document/Scanned Page or Image in which text are available to ASCII Character that a Computer can Recognize.
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.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Presentations from our AI Meetup #3 you can find below. We talked about soil segmentation, deep learning in Automotive, The Usage of the YOLO Algorithm for Traffic Participants Detection.
Presentations are really educational #machinelearning! #artificialintelligence #deeplearning #automotive #ai #novisadai
Towards neuralprocessingofgeneralpurposeapproximateprogramsParidha Saxena
Did validation of one of the machine learning algorithms of neural networks,and compared the results for its implementation on hardware (FPGA) using xilinx, with that of a sequential code execution(using FANN).
Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution (or cross-correlation) kernels, only 25 neurons are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features.
CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input.
Feed-forward neural networks are usually fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "full connectivity" of these networks make them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) Robust datasets also increases the probability that CNNs will learn the generalized principles that characterize a given dataset rather than the biases of a poorly-populated set.
Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.
CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns to optimize the filters (or kernels) through automated learning, whereas in traditional algorithms these filters are hand-engineered. This independence from prior knowledge and human intervention in feature extraction is a major advantage.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
New Approach of Preprocessing For Numeral RecognitionIJERA Editor
The present paper proposes a new approach of preprocessing for handwritten, printed and isolated numeral
characters. The new approach reduces the size of the input image of each numeral by discarding the redundant
information. This method reduces also the number of features of the attribute vector provided by the extraction
features method. Numeral recognition is carried out in this work through k nearest neighbors and multilayer
perceptron techniques. The simulations have obtained a good rate of recognition in fewer running time.
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
CAPTCHA and Convolutional neural network Bushra Jbawi
CAPTCHA is not a good choice for security those days
because we can break it by Convolutional neural network
Topics :
what is CAPTCHA ?
how can we break CAPTCHA ?
what is the architecture of CNN ?
Similar to An approach to empirical Optical Character recognition paradigm using Multi-Layer Perceptorn Neural Network (20)
A statistical model or a machine learning algorithm is said to have underfitting when a model is too simple to capture data complexities. It represents the inability of the model to learn the training data effectively result in poor performance both on the training and testing data. In simple terms, an underfit model’s are inaccurate, especially when applied to new, unseen examples. It mainly happens when we uses very simple model with overly simplified assumptions. To address underfitting problem of the model, we need to use more complex models, with enhanced feature representation, and less regularization.
A statistical model is said to be overfitted when the model does not make accurate predictions on testing data. When a model gets trained with so much data, it starts learning from the noise and inaccurate data entries in our data set. And when testing with test data results in High variance. Then the model does not categorize the data correctly, because of too many details and noise. The causes of overfitting are the non-parametric and non-linear methods because these types of machine learning algorithms have more freedom in building the model based on the dataset and therefore they can really build unrealistic models. A solution to avoid overfitting is using a linear algorithm if we have linear data or using the parameters like the maximal depth if we are using decision trees.
A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition[4] or speech recognition. The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled.
Additional stored states and the storage under direct control by the network can be added to both infinite-impulse and finite-impulse networks. The storage can also be replaced by another network or graph if that incorporates time delays or has feedback loops. Such controlled states are referred to as gated state or gated memory, and are part of long short-term memory networks (LSTMs) and gated recurrent units. This is also called Feedforward Neural Network (FNN). Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs.
Random Forest Algorithm widespread popularity stems from its user-friendly nature and adaptability, enabling it to tackle both classification and regression problems effectively. The algorithm’s strength lies in its ability to handle complex datasets and mitigate overfitting, making it a valuable tool for various predictive tasks in machine learning.
One of the most important features of the Random Forest Algorithm is that it can handle the data set containing continuous variables, as in the case of regression, and categorical variables, as in the case of classification. It performs better for classification and regression tasks. In this tutorial, we will understand the working of random forest and implement random forest on a classification task.
Principal Component Analysis(PCA) technique was introduced by the mathematician Karl Pearson in 1901. It works on the condition that while the data in a higher dimensional space is mapped to data in a lower dimension space, the variance of the data in the lower dimensional space should be maximum.
Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation that converts a set of correlated variables to a set of uncorrelated variables.PCA is the most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover,
Principal Component Analysis (PCA) is an unsupervised learning algorithm technique used to examine the interrelations among a set of variables. It is also known as a general factor analysis where regression determines a line of best fit.
The main goal of Principal Component Analysis (PCA) is to reduce the dimensionality of a dataset while preserving the most important patterns or relationships between the variables without any prior knowledge of the target variables.
Principal Component Analysis (PCA) is used to reduce the dimensionality of a data set by finding a new set of variables, smaller than the original set of variables, retaining most of the sample’s information, and useful for the regression and classification of data.
The best known natural language processing tool is GPT-3, from OpenAI, which uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call tools like this “language models,” and they can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. Language models are already reshaping traditional text analytics, but GPT-3 was an especially pivotal language model because, at 10x larger than any previous model upon release, it was the first large language model, which enabled it to perform even more advanced tasks like programming and solving high school–level math problems. The latest version, called InstructGPT, has been fine-tuned by humans to generate responses that are much better aligned with human values and user intentions, and Google’s latest model shows further impressive breakthroughs on language and reasoning.
For businesses, the three areas where GPT-3 has appeared most promising are writing, coding, and discipline-specific reasoning. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. This tool, Codex, is already powering products like Copilot for Microsoft’s subsidiary GitHub and is capable of creating a basic video game simply by typing instructions. This transformative capability was already expected to change the nature of how programmers do their jobs, but models continue to improve — the latest from Google’s DeepMind AI lab, for example, demonstrates the critical thinking and logic skills necessary to outperform most humans in programming competitions.
Models like GPT-3 are considered to be foundation models — an emerging AI research area — which also work for other types of data such as images and video. Foundation models can even be trained on multiple forms of data at the same time, like OpenAI’s DALL·E 2, which is trained on language and images to generate high-resolution renderings of imaginary scenes or objects simply from text prompts. Due to their potential to transform the nature of cognitive work, economists expect that foundation models may affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.
It is a classification technique based on Bayes’ Theorem with an independence assumption among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
The Naïve Bayes classifier is a popular supervised machine learning algorithm used for classification tasks such as text classification. It belongs to the family of generative learning algorithms, which means that it models the distribution of inputs for a given class or category. This approach is based on the assumption that the features of the input data are conditionally independent given the class, allowing the algorithm to make predictions quickly and accurately.
In statistics, naive Bayes classifiers are considered as simple probabilistic classifiers that apply Bayes’ theorem. This theorem is based on the probability of a hypothesis, given the data and some prior knowledge. The naive Bayes classifier assumes that all features in the input data are independent of each other, which is often not true in real-world scenarios. However, despite this simplifying assumption, the naive Bayes classifier is widely used because of its efficiency and good performance in many real-world applications.
Moreover, it is worth noting that naive Bayes classifiers are among the simplest Bayesian network models, yet they can achieve high accuracy levels when coupled with kernel density estimation. This technique involves using a kernel function to estimate the probability density function of the input data, allowing the classifier to improve its performance in complex scenarios where the data distribution is not well-defined. As a result, the naive Bayes classifier is a powerful tool in machine learning, particularly in text classification, spam filtering, and sentiment analysis, among others.
For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.
An NB model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
The field of artificial neural networks is often just called neural networks or multi-layer perceptrons after perhaps the most useful type of neural network. A perceptron is a single neuron model that was a precursor to larger neural networks.
It is a field that investigates how simple models of biological brains can be used to solve difficult computational tasks like the predictive modeling tasks we see in machine learning. The goal is not to create realistic models of the brain but instead to develop robust algorithms and data structures that we can use to model difficult problems.
The power of neural networks comes from their ability to learn the representation in your training data and how best to relate it to the output variable you want to predict. In this sense, neural networks learn mapping. Mathematically, they are capable of learning any mapping function and have been proven to be a universal approximation algorithm.
The predictive capability of neural networks comes from the hierarchical or multi-layered structure of the networks. The data structure can pick out (learn to represent) features at different scales or resolutions and combine them into higher-order features, for example, from lines to collections of lines to shapes.
Long short-term memory (LSTM) network is a recurrent neural network (RNN), aimed to deal with the vanishing gradient problem present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands of timesteps, thus "long short-term memory". It is applicable to classification, processing and predicting data based on time series, such as in handwriting, speech recognition, machine translation, speech activity detection, robot control, video games, and healthcare.
A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell. Forget gates decide what information to discard from a previous state by assigning a previous state, compared to a current input, a value between 0 and 1. A (rounded) value of 1 means to keep the information, and a value of 0 means to discard it. Input gates decide which pieces of new information to store in the current state, using the same system as forget gates. Output gates control which pieces of information in the current state to output by assigning a value from 0 to 1 to the information, considering the previous and current states. Selectively outputting relevant information from the current state allows the LSTM network to maintain useful, long-term dependencies to make predictions, both in current and future time-steps.
linear regression is a linear approach for modelling a predictive relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables), which are measured without error. The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. If the explanatory variables are measured with error then errors-in-variables models are required, also known as measurement error models.
In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.
Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications.[4] This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.
Linear regression has many practical uses. Most applications fall into one of the following two broad categories:
If the goal is error reduction in prediction or forecasting, linear regression can be used to fit a predictive model to an observed data set of values of the response and explanatory variables. After developing such a model, if additional values of the explanatory variables are collected without an accompanying response value, the fitted model can be used to make a prediction of the response.
If the goal is to explain variation in the response variable that can be attributed to variation in the explanatory variables, linear regression analysis can be applied to quantify the strength of the relationship between the response and the explanatory variables, and in particular to determine whether some explanatory variables may have no linear relationship with the response at all, or to identify which subsets of explanatory variables may contain redundant information about the response.
The K-Nearest Neighbors (KNN) algorithm is a robust and intuitive machine learning method employed to tackle classification and regression problems. By capitalizing on the concept of similarity, KNN predicts the label or value of a new data point by considering its K closest neighbours in the training dataset. In this article, we will learn about a supervised learning algorithm (KNN) or the k – Nearest Neighbours, highlighting it’s user-friendly nature.
What is the K-Nearest Neighbors Algorithm?
K-Nearest Neighbours is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining, and intrusion detection.
It is widely disposable in real-life scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about the distribution of data (as opposed to other algorithms such as GMM, which assume a Gaussian distribution of the given data). We are given some prior data (also called training data), which classifies coordinates into groups identified by an attribute.
The Hidden Markov model (HMM) is a statistical model that was first proposed by Baum L.E. (Baum and Petrie, 1966) and uses a Markov process that contains hidden and unknown parameters. In this model, the observed parameters are used to identify the hidden parameters. These parameters are then used for further analysis. The HMM is a type of Markov chain. Its state cannot be directly observed but can be identified by observing the vector series. Since the 1980s, HMM has been successfully used for speech recognition, character recognition, and mobile communication techniques. It has also been rapidly adopted in such fields as bioinformatics and fault diagnosis. The basic principle of HMM is that the observed events have no one-to-one correspondence with states but are linked to states through the probability distribution. It is a doubly stochastic process, which includes a Markov chain as the basic stochastic process, and describes state transitions and stochastic processes that describe the statistical correspondence between the states and observed values. From the perspective of observers, only the observed value can be viewed, while the states cannot. A stochastic process is used to identify the existence of states and their characteristics. Thus, it is called a “hidden” Markov model.
Statistical methods are used to build state changes in HMM to understand the most possible trends in the surveillance data. HMM can automatically and flexibly adjust the trends, seasonal, covariant, and distributional elements. HMM has been used in many studies on time series surveillance data. For example, Le Strat and Carrat used a univariate HMM to handle influenza-like time series data in France. Additionally, Madigan indicated that HMM needed to include spatial information based on existing states.
One of the first uses of ensemble methods was the bagging technique. This technique was developed to overcome instability in decision trees. In fact, an example of the bagging technique is the random forest algorithm. The random forest is an ensemble of multiple decision trees. Decision trees tend to be prone to overfitting. Because of this, a single decision tree can’t be relied on for making predictions. To improve the prediction accuracy of decision trees, bagging is employed to form a random forest. The resulting random forest has a lower variance compared to the individual trees.
The success of bagging led to the development of other ensemble techniques such as boosting, stacking, and many others. Today, these developments are an important part of machine learning.
The many real-life machine learning applications show these ensemble methods’ importance. These applications include many critical systems. These include decision-making systems, spam detection, autonomous vehicles, medical diagnosis, and many others. These systems are crucial because they have the ability to impact human lives and business revenues. Therefore ensuring the accuracy of machine learning models is paramount. An inaccurate model can lead to disastrous consequences for many businesses or organizations. At worst, they can lead to the endangerment of human lives.
Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains.
An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.
Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
In this slide we have coverd the following topices
What is Artificial Intelligence?
What is Machine Learning?
Relationship among AI, ML and DL.
Human Brain Learning Process
Learning Vs Recognition
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Definition of Reinforcement Learning
Reinforcement Learning Application: AWS Deep racer
Markov Decision Process
Understanding Q-Learning Algorithm
Q-Learning Algorithm Example
Session on evaluation of DevSecOps. This tutorial is made the very basic process of the DevOps cycle for the beginner level. So sometimes we won’t use very deep technical terms to understand.
DevOps is a set of practices that aims to provide superior quality software quickly by integrating the processes between the development and the operation teams. DevOps is an agile relationship between development and IT operations. DevOps is the abbreviation for Development and Operations. The development includes Plan, Create, Verify and Package. Operations include Release, Configure, and Monitor.
When developing software with Python, a basic approach is to install Python on your machine, install all your required libraries via the terminal and write all of the source code. This works fine for simple Python scripting projects.
Final project report on grocery store management system..pdfKamal Acharya
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Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
An approach to empirical Optical Character recognition paradigm using Multi-Layer Perceptorn Neural Network
1. An approach to empirical Optical
Character recognition paradigm
using Multi-Layer Perceptorn
Neural Network
2. OUTLINEOUTLINE
IntroductionIntroduction
What is Optical Character RecognitionWhat is Optical Character Recognition
OCR ModelOCR Model
Acquiring ImageAcquiring Image
Input PreprocessingInput Preprocessing
Input SegmentationInput Segmentation
Feature ExtractionFeature Extraction
Multi-Layer Perceptron Neural NetworkMulti-Layer Perceptron Neural Network
TrainingTraining
RecognitionRecognition
Experimental ResultExperimental Result
ConclusionsConclusions
ReferencesReferences
Dec 22, 2015 2An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
3. Dec 22, 2015 3An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Representing the architecture of Optical Character
Recognition(OCR) that is designed using artificial
computational model same as biological neuron network.
Introduction
4. What isWhat is OOpticalptical CCharacterharacter RRecognitionecognition??
OCR allow to convert mechanical or electronic
image base text into the machine encodes able text
through an optical mechanism.
The ultimate objective of OCR is to simulate
the human reading capabilities so the computer
can read, understand, edit and do similar activities
it does with the text.
Dec 22, 2015 4An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
5. OCR ModelOCR Model
Figure 1:Figure 1: Optical Character Recognizer Model.
Dec 22, 2015 5An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
6. Acquiring ImageAcquiring Image
- Image is
acquisition from
any possible
source that can
be hardware
device like
camera, scanner
and so on.
Dec 22, 2015 6An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
7. Input PreprocessingInput Preprocessing
- Image processing is a signal processing that
convert either an image or a set of
characteristics or parameters related to the
image.
-It is achieve correction of distortion, noise
reduction, normalization, filtering the image
and so on.
Dec 22, 2015 7An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
8. Input PreprocessingInput Preprocessing
- The RGB color space contains red, green,
blue that are added together in a variety of
ways to reproduce a array of color.
Dec 22, 2015 8An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
RGB to Gray scale Conversion
Input ImageRGB to Gray scale Conversion
9. Input PreprocessingInput Preprocessing
- A binary image has only two possible color
value for each pixel is that black and white.
This color depth is 1-bit monochrome.
Dec 22, 2015 9An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Gray scale to Binary Image Conversion
RGB to Gray scale ImageGray scale to Binary image
10. InputInput Segmentation
- By the Image segmentation simplify and/or
change the representation of an image into
something that is more meaningful and easier
to analyze.
-Image segmentation is used for object
recognition of an image; detect the boundary
estimation, image editing or image database
look-up.
Dec 22, 2015 10An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
11. InputInput Segmentation
- Enumeration of character lines in a
character image is essential in delimiting the
bounds within which the detection can
precede.
Dec 22, 2015 11An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Determining Character Line
Figure-6: Boundary detection of character line.
12. InputInput Segmentation
- Detection of individual symbols involves
scanning character lines for orthogonally
separable images composed of black pixels.
Dec 22, 2015 12An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Detecting Individual Character
Figure-7: Boundary detection of a character.
13. Feature ExtractionFeature Extraction
- Feature extraction extract set of feature to
produce the relevant information from the
original input set data that can be represent
in a lower dimensionality space.
-To implement the feature extraction process
we have used Image to matrix mapping
process.
Dec 22, 2015 13An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
14. Feature ExtractionFeature Extraction
- By the matrix mapping process the character
image is converted corresponding two
dimensional binary matrixes.
Dec 22, 2015 14An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Image to Matrix Mapping
Image to
Matrix
Mapping
Image to
Matrix
Mapping
Binary
Represe
ntation
Binary
Represe
ntation
15. Multi-Layer Perceptron Neural NetworkMulti-Layer Perceptron Neural Network
Dec 22, 2015 15An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Multi-Layer Perceptron Neural network has an input
layer, hidden layer and output layer. Input layer feed the
input data set that is came from feature extraction and
output layer produced the set of output vector.
16. TrainingTraining
Dec 22, 2015 16An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Appling the learning process algorithm within the
multilayer network architecture, the synaptic weights
and threshold are update in a way that the
classification/recognition task can be performing
efficiently.
Presenting 600-602-6 three Layer Neural network
architecture to perform the Optical Character
Recognition Learning process.
17. Algorithm (sequential)
1. Apply an input vector and calculate all activations, a and u
2. Evaluate ∆k for all output units via:
(Note similarity to perceptron learning algorithm)
3. Backpropagate ∆ks to get error terms δ for hidden layers using:
4. Evaluate changes using:
))(('))()(()( tagtytdt iiii −=∆
∑∆=
k
kikii wttugt )())((')(δ
)()()()1(
)()()()1(
tzttwtw
txttvtv
jiijij
jiijij
∆+=+
+=+
η
ηδ
Dec 22, 2015 An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network 17
18. Once weight changes are computed for all units, weights are updated
at the same time (bias included as weights here). An example:
y1
y2
x1
x2
v11= -1
v21= 0
v12= 0
v22= 1
v10= 1
v20= 1
w11= 1
w21= -1
w12= 0
w22= 1
Use identity activation function (ie g(a) = a)
Dec 22, 2015 An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network 18
19. All biases set to 1. Will not draw them for clarity.
Learning rate η = 0.1
y1
y2
x1
x2
v11= -1
v21= 0
v12= 0
v22= 1
w11= 1
w21= -1
w12= 0
w22= 1
Have input [0 1] with target [1 0].
x1= 0
x2= 1
Dec 22, 2015 An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network 19
28. And are multiplied by inputs:
∆1= -1
∆2= -2
v11= -1
v21= 0
v12= 0
v22= 1
δ1 x1 = 0
δ2 x2 = -2
)()()()1( txttvtv jiijij ηδ=−+
x2= 1
x1= 0
δ2 x1 = 0
δ1 x2 = 1
Dec 22, 2015 An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network 28
29. Finally change weights:
v11= -1
v21= 0
v12= 0.1
v22= 0.8
)()()()1( txttvtv jiijij ηδ=−+
x2= 1
x1= 0 w11= 0.9
w21= -1.2
w12= -0.2
w22= 0.6
Note that the weights multiplied by the zero input are unchanged as they do
not contribute to the error
We have also changed biases (not shown)
Dec 22, 2015 An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network 29
30. Now go forward again (would normally use a new input vector):
v11= -1
v21= 0
v12= 0.1
v22= 0.8
)()()()1( txttvtv jiijij ηδ=−+
x2= 1
x1= 0 w11= 0.9
w21= -1.2
w12= -0.2
w22= 0.6
z2 = 1.6
z1 = 1.2
Dec 22, 2015 An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network 30
31. Now go forward again (would normally use a new input vector):
v11= -1
v21= 0
v12= 0.1
v22= 0.8
)()()()1( txttvtv jiijij ηδ=−+
x2= 1
x1= 0 w11= 0.9
w21= -1.2
w12= -0.2
w22= 0.6
y2 = 0.32
y1 = 1.66
Outputs now closer to target value [1, 0]
Dec 22, 2015 An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network 31
32. RecognitionRecognition
Dec 22, 2015 32An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Feature data is feed to the network input layer and
produced an output vector and calculating the error
function.
33. Experimental ResultExperimental Result
Dec 22, 2015 33An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Chart-1: Isolated Character Recognition
experiment result comparison.
62 English character (i.e, English
Capital Alphabets A to Z, English
Small Alphabets a to z, English
Numerical Digits 0 to 9) image
recognition.
So the average Success rate for the
Isolated Character Recognition is =
91.53%
Isolated Character Recognition
34. Experimental ResultExperimental Result
Dec 22, 2015 34An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Chart-2: Sentential Case Character Recognition
experiment result comparison.
For the sentential case character we
have used the sentence is “A Quick
Brown Fox Jumps over the Lazy
Dog.” This experimental sentence
is written four different type of font
like Arial, Calibri (body), Segoe UI
and Times New Roman.
So the average Success rate for the
sentential case Character
Recognition is = 80.65%
Sentential Case Character Recognition
0
20
40
60
80
100
Correct
Recognition
Wrong
Recognition
Success(%)
Error(%)
35. ConclusionsConclusions
In our proposed system are achieved 91.53%
accuracy for the isolated character and 80.65%
accuracy for the sentential case character.
In future we try to be improved the accuracy of
the OCR model by better preprocessing method
and optimal ANN architecture.
Dec 22, 2015 35An approach to empirical OCR paradigm using Multi-Layer Perceptorn Neural Network
Editor's Notes
OCR is the contraction for Optical Character Recognition is a technology that allows converting mechanical or electronic image base text into the machine encodes able text through an optical mechanism
The ultimate objective of any OCR system is to simulate the human reading capabilities so the computer can read, understand, edit and do similar activities it does with the text.
Automatic number plate recognition for house address, vehicle and something like this; Read the business card information; Convert to textual format from a scanning or printed document or book; Converting the handwriting character
The databases collect the observations required for parameter estimations.