The document discusses optical character recognition (OCR) and intelligent character recognition (ICR). It describes the process of OCR/ICR which involves pre-processing scanned documents, segmenting text into characters, extracting features of the characters, and classifying the characters. The pre-processing steps prepare the images for analysis and include binarization, noise removal, and correction of skew and slant. Feature extraction involves statistical techniques like zoning, projections, and distances to represent character images. Classification then identifies the characters using algorithms like k-nearest neighbor or neural networks.
Representation and recognition of handwirten digits using deformable templatesAhmed Abd-Elwasaa
This document presents a case study on using deformable templates for recognizing handwritten digits. Deformable templates match unknown images to known templates by deforming the contours of templates to fit the edge strengths of unknown images. The dissimilarity measure is derived from the deformation needed for the match. This technique achieved recognition rates up to 99.25% on a dataset of 2,000 handwritten digits. Statistical and structural features are extracted to represent characters for classification using deformable templates.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
The document discusses optical character recognition (OCR), including its history, current capabilities, and challenges. OCR is a technology that uses optical mechanisms to automatically recognize text characters, similar to how humans read. It involves converting scanned images of text into machine-encoded text. The summary discusses some of the key difficulties in OCR, such as distinguishing similar characters like 'O' and '0' or interpreting text against backgrounds. It also provides an overview of the paper, which will analyze the advancements and limitations of existing OCR systems to determine if it is suitable for different needs.
This document provides a comprehensive review of offline handwritten character recognition. It discusses the various phases of a character recognition system, including image acquisition, preprocessing, segmentation, feature extraction, and classification. Preprocessing techniques like noise removal, binarization, and size normalization are described. Common feature extraction methods like statistical, transform, and structural features are also summarized. Finally, the document analyzes different classification approaches used in character recognition, such as neural networks, support vector machines, and multiple classifier methods.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
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.
A Review on Geometrical Analysis in Character Recognitioniosrjce
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 document provides a review of existing methods for handwritten character recognition based on geometrical properties. It begins by classifying character recognition as either printed or handwritten, and describes the different phases a character recognition system typically includes: image acquisition, preprocessing, segmentation, feature extraction, and classification. Preprocessing steps like binarization, noise removal, normalization and morphological operations are discussed. Feature extraction methods focused on include statistical, global and structural features. Geometrical features involving lines, loops, strokes and their directions are highlighted. Classification algorithms mentioned are neural networks, SVM, k-nearest neighbor, and genetic algorithms. The literature review provides examples of character recognition research using geometrical features like horizontal/vertical line analysis and directional feature
SCENE TEXT RECOGNITION IN MOBILE APPLICATION BY CHARACTER DESCRIPTOR AND STRU...Cheriyan K M
In text detection, our
previously proposed algorithms are applied to obtain text regions
from scene image. First, we design a discriminative character
descriptor by combining several state-of-the-art feature detectors
and descriptors. Second, we model character structure at each
character class by designing stroke configuration maps.
Representation and recognition of handwirten digits using deformable templatesAhmed Abd-Elwasaa
This document presents a case study on using deformable templates for recognizing handwritten digits. Deformable templates match unknown images to known templates by deforming the contours of templates to fit the edge strengths of unknown images. The dissimilarity measure is derived from the deformation needed for the match. This technique achieved recognition rates up to 99.25% on a dataset of 2,000 handwritten digits. Statistical and structural features are extracted to represent characters for classification using deformable templates.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
The document discusses optical character recognition (OCR), including its history, current capabilities, and challenges. OCR is a technology that uses optical mechanisms to automatically recognize text characters, similar to how humans read. It involves converting scanned images of text into machine-encoded text. The summary discusses some of the key difficulties in OCR, such as distinguishing similar characters like 'O' and '0' or interpreting text against backgrounds. It also provides an overview of the paper, which will analyze the advancements and limitations of existing OCR systems to determine if it is suitable for different needs.
This document provides a comprehensive review of offline handwritten character recognition. It discusses the various phases of a character recognition system, including image acquisition, preprocessing, segmentation, feature extraction, and classification. Preprocessing techniques like noise removal, binarization, and size normalization are described. Common feature extraction methods like statistical, transform, and structural features are also summarized. Finally, the document analyzes different classification approaches used in character recognition, such as neural networks, support vector machines, and multiple classifier methods.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
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.
A Review on Geometrical Analysis in Character Recognitioniosrjce
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 document provides a review of existing methods for handwritten character recognition based on geometrical properties. It begins by classifying character recognition as either printed or handwritten, and describes the different phases a character recognition system typically includes: image acquisition, preprocessing, segmentation, feature extraction, and classification. Preprocessing steps like binarization, noise removal, normalization and morphological operations are discussed. Feature extraction methods focused on include statistical, global and structural features. Geometrical features involving lines, loops, strokes and their directions are highlighted. Classification algorithms mentioned are neural networks, SVM, k-nearest neighbor, and genetic algorithms. The literature review provides examples of character recognition research using geometrical features like horizontal/vertical line analysis and directional feature
SCENE TEXT RECOGNITION IN MOBILE APPLICATION BY CHARACTER DESCRIPTOR AND STRU...Cheriyan K M
In text detection, our
previously proposed algorithms are applied to obtain text regions
from scene image. First, we design a discriminative character
descriptor by combining several state-of-the-art feature detectors
and descriptors. Second, we model character structure at each
character class by designing stroke configuration maps.
Optical Character Recognition (OCR) based RetrievalBiniam Asnake
The document outlines research works on optical character recognition (OCR) systems, including both global and local (Amharic language) research. It discusses several local studies from 1997-2011 focused on developing OCR for printed, typewritten and handwritten Amharic text. The studies explored various preprocessing, segmentation, recognition algorithms and achieved recognition accuracy rates ranging from 15-99% depending on the type of Amharic text and techniques used. Future research directions included improving techniques for formatted text, different font styles and improving accuracy.
This document provides an overview of optical character recognition (OCR) including system requirements, advantages and disadvantages, operation and management, questionnaire design, OCR field operation, and country outlook. It describes OCR as a system that scans printed or handwritten text and converts it into machine-readable text. Key aspects covered include scanner specifications, recognition software, training needs, error sources and mitigation, and common practices in countries using OCR for censuses.
The document discusses optical character recognition for Urdu handwriting. It introduces OCR and its applications. It then discusses earlier work on OCR systems that were font-specific. The document outlines the steps in OCR including image acquisition, preprocessing, segmentation, feature extraction, classification, and recognition. It provides an overview of the Urdu script and its variations. The document then summarizes research conducted on recognizing offline isolated Urdu characters using moment invariants and support vector machines. Other works discussed include an online and offline OCR system for Urdu using a segmentation-free approach, classifying Urdu ligatures using convolutional neural networks, and a segmentation-based approach for Urdu Nastaliq script recognition.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
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 document provides a comprehensive review of existing works in offline handwritten character recognition. It discusses the three major stages of any character recognition system: preprocessing, feature extraction, and classification. For preprocessing, it describes techniques like binarization, filtering, and morphological operations that are used to improve image quality. For feature extraction, it discusses various methods used to represent characters, including global transformations, statistical representations, and geometrical/topological features. Wavelet transforms are highlighted as a commonly used feature extraction technique. Finally, it provides an overview of literature on methods used in each stage of offline handwritten character recognition systems.
"FingerPrint Recognition Using Principle Component Analysis(PCA)”Er. Arpit Sharma
Fingerprint recognition is one of the oldest and most popular biometric technologies and it is used in criminal investigations, civilian, commercial applications, and so on. Fingerprint matching is the process used to determine whether the two sets of fingerprints details come from the same finger or not. This work focuses on feature extraction and minutiae matching stage. There are many matching techniques used for fingerprint recognition systems such as minutiae based matching, pattern based matching, Correlation based matching, and image based matching.
A new method based upon Principal Component Analysis (PCA) for fingerprint enhancement is proposed in this paper. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. In the proposed method image is first decomposed into directional images using decimation free Directional Filter bank DDFB. Then PCA is applied to these directional fingerprint images which gives the PCA filtered images. Which are basically directional images? Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.
The document summarizes the key steps in an optical character recognition (OCR) system for recognizing printed text:
1. Image acquisition involves obtaining the image, which can be done using scanners or digital cameras.
2. Pre-processing prepares the image for recognition through techniques like converting to grayscale, skew correction, binarization, noise reduction, and thinning.
3. Segmentation separates the image into lines and individual characters.
4. Recognition identifies the characters by comparing features or templates to stored models.
The paper then discusses specific algorithms that could implement grayscale conversion, skew correction, and other steps in the OCR system.
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.
Improvement of the Recognition Rate by Random ForestIJERA Editor
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures
Improvement oh the recognition rate by random forestYoussef Rachidi
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
In this paper; we introduce a system of automatic recognition of Amazigh characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal, Gabor filters and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the Support vector machines (SVM) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
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.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
IRJET- Optical Character Recognition using Image ProcessingIRJET Journal
This document discusses optical character recognition (OCR) using image processing. It begins with an abstract that defines OCR as the conversion of typed, handwritten, or printed text into machine-encoded text from scanned documents or photos. The document then outlines the components and steps of a typical OCR system, including optical scanning, preprocessing, segmentation, character extraction, and recognition. It describes using techniques like thresholding, smoothing, projection profiles, clustering algorithms, and creating a character database to classify and recognize characters for conversion to machine-encoded text.
PORTABLE CAMERA-BASED ASSISTIVE TEXT AND PRODUCT LABEL READING FROM HAND- H...Sathmica K
The document describes a camera-based assistive technology framework for blind persons to read text labels on hand-held objects. It uses computer vision and optical character recognition (OCR) techniques. Specifically, it proposes a motion-based method to isolate the object of interest in the camera view. Text is then localized and recognized on the isolated region. The recognized text is output verbally to the blind user via speech. The framework has three main components - scene capture via camera, data processing for object detection, text localization and recognition, and audio output of the text to the user.
Optical character recognition (OCR) is a technology that converts images of typed, handwritten or printed text into machine-encoded text. The document describes the OCR process which includes image pre-processing, segmentation, feature extraction and recognition using a multi-layer perceptron neural network. It discusses advantages such as increased efficiency and ability to instantly search text. Disadvantages include issues with low quality documents. Applications include data entry for business documents and making printed documents searchable.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
A Survey of Modern Character Recognition Techniquesijsrd.com
This document summarizes several modern techniques for handwritten character recognition. It discusses common feature extraction methods like statistical, structural and global transformation features. It then summarizes several papers that have proposed different techniques for handwritten character recognition, including using associative memory nets, moment invariants with support vector machines, neural networks, hidden markov models, gradient features, and multi-scale neural networks. The document concludes that neural networks are commonly used for training, and that feature extraction methods continue to be improved, but handwritten character recognition remains an active area of research.
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.
Optical Character Recognition (OCR) based RetrievalBiniam Asnake
The document outlines research works on optical character recognition (OCR) systems, including both global and local (Amharic language) research. It discusses several local studies from 1997-2011 focused on developing OCR for printed, typewritten and handwritten Amharic text. The studies explored various preprocessing, segmentation, recognition algorithms and achieved recognition accuracy rates ranging from 15-99% depending on the type of Amharic text and techniques used. Future research directions included improving techniques for formatted text, different font styles and improving accuracy.
This document provides an overview of optical character recognition (OCR) including system requirements, advantages and disadvantages, operation and management, questionnaire design, OCR field operation, and country outlook. It describes OCR as a system that scans printed or handwritten text and converts it into machine-readable text. Key aspects covered include scanner specifications, recognition software, training needs, error sources and mitigation, and common practices in countries using OCR for censuses.
The document discusses optical character recognition for Urdu handwriting. It introduces OCR and its applications. It then discusses earlier work on OCR systems that were font-specific. The document outlines the steps in OCR including image acquisition, preprocessing, segmentation, feature extraction, classification, and recognition. It provides an overview of the Urdu script and its variations. The document then summarizes research conducted on recognizing offline isolated Urdu characters using moment invariants and support vector machines. Other works discussed include an online and offline OCR system for Urdu using a segmentation-free approach, classifying Urdu ligatures using convolutional neural networks, and a segmentation-based approach for Urdu Nastaliq script recognition.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
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 document provides a comprehensive review of existing works in offline handwritten character recognition. It discusses the three major stages of any character recognition system: preprocessing, feature extraction, and classification. For preprocessing, it describes techniques like binarization, filtering, and morphological operations that are used to improve image quality. For feature extraction, it discusses various methods used to represent characters, including global transformations, statistical representations, and geometrical/topological features. Wavelet transforms are highlighted as a commonly used feature extraction technique. Finally, it provides an overview of literature on methods used in each stage of offline handwritten character recognition systems.
"FingerPrint Recognition Using Principle Component Analysis(PCA)”Er. Arpit Sharma
Fingerprint recognition is one of the oldest and most popular biometric technologies and it is used in criminal investigations, civilian, commercial applications, and so on. Fingerprint matching is the process used to determine whether the two sets of fingerprints details come from the same finger or not. This work focuses on feature extraction and minutiae matching stage. There are many matching techniques used for fingerprint recognition systems such as minutiae based matching, pattern based matching, Correlation based matching, and image based matching.
A new method based upon Principal Component Analysis (PCA) for fingerprint enhancement is proposed in this paper. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. In the proposed method image is first decomposed into directional images using decimation free Directional Filter bank DDFB. Then PCA is applied to these directional fingerprint images which gives the PCA filtered images. Which are basically directional images? Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.
The document summarizes the key steps in an optical character recognition (OCR) system for recognizing printed text:
1. Image acquisition involves obtaining the image, which can be done using scanners or digital cameras.
2. Pre-processing prepares the image for recognition through techniques like converting to grayscale, skew correction, binarization, noise reduction, and thinning.
3. Segmentation separates the image into lines and individual characters.
4. Recognition identifies the characters by comparing features or templates to stored models.
The paper then discusses specific algorithms that could implement grayscale conversion, skew correction, and other steps in the OCR system.
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.
Improvement of the Recognition Rate by Random ForestIJERA Editor
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures
Improvement oh the recognition rate by random forestYoussef Rachidi
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
In this paper; we introduce a system of automatic recognition of Amazigh characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal, Gabor filters and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the Support vector machines (SVM) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
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.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
IRJET- Optical Character Recognition using Image ProcessingIRJET Journal
This document discusses optical character recognition (OCR) using image processing. It begins with an abstract that defines OCR as the conversion of typed, handwritten, or printed text into machine-encoded text from scanned documents or photos. The document then outlines the components and steps of a typical OCR system, including optical scanning, preprocessing, segmentation, character extraction, and recognition. It describes using techniques like thresholding, smoothing, projection profiles, clustering algorithms, and creating a character database to classify and recognize characters for conversion to machine-encoded text.
PORTABLE CAMERA-BASED ASSISTIVE TEXT AND PRODUCT LABEL READING FROM HAND- H...Sathmica K
The document describes a camera-based assistive technology framework for blind persons to read text labels on hand-held objects. It uses computer vision and optical character recognition (OCR) techniques. Specifically, it proposes a motion-based method to isolate the object of interest in the camera view. Text is then localized and recognized on the isolated region. The recognized text is output verbally to the blind user via speech. The framework has three main components - scene capture via camera, data processing for object detection, text localization and recognition, and audio output of the text to the user.
Optical character recognition (OCR) is a technology that converts images of typed, handwritten or printed text into machine-encoded text. The document describes the OCR process which includes image pre-processing, segmentation, feature extraction and recognition using a multi-layer perceptron neural network. It discusses advantages such as increased efficiency and ability to instantly search text. Disadvantages include issues with low quality documents. Applications include data entry for business documents and making printed documents searchable.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
A Survey of Modern Character Recognition Techniquesijsrd.com
This document summarizes several modern techniques for handwritten character recognition. It discusses common feature extraction methods like statistical, structural and global transformation features. It then summarizes several papers that have proposed different techniques for handwritten character recognition, including using associative memory nets, moment invariants with support vector machines, neural networks, hidden markov models, gradient features, and multi-scale neural networks. The document concludes that neural networks are commonly used for training, and that feature extraction methods continue to be improved, but handwritten character recognition remains an active area of research.
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.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
2. Is a system that provides a full alphanumeric recognition of printed or
handwritten characters at electronic speed by simply scanning the form
OCR : Optical Character Recognition
Intelligent Character Recognition (ICR) is used to describe the process of
interpreting image data, in particular alphanumeric text.
Sometimes OCR is known as ICR
3. Functions & Features of OCR
• Forms can be scanned through a scanner and then the recognition
engine of the OCR system interpret the images and turn images of
handwritten or printed characters into ASCII data (machine-readable
characters).
• The technology provides a complete form processing and
documents capture solution.
• Allows an open, scalable and workflow.
• Includes forms definition, scanning, image
• pre-processing, and recognition capabilities.
4. ICR,OCR and OMR Differences
• ICR and OCR are recognition engines used with imaging;
• OMR is a data collection technology that does not require a
recognition engine.
• OMR cannot recognize hand-printed or machine-printed
characters.
5. Optical Mark Reader (OMR)
• Forms
• An OMR works with a specialized document and contains timing
tracks along one edge of the form to indicate scanner where to read
for marks which look like black boxes on the top or bottom of a
form.
• The cut of the form is very precise and the bubbles on a form must
be located in the same location on every form.
• Storage
• With OMR, the image of a document is not scanned and stored.
• Accuracy
• OMR is simpler than OCR.
• designed properly, OMR has more accuracy than OCR.
6. OCR/ ICR
• Forms
• OCR/ ICR is more flexible since no timing tracks or block like form
IDs required.
• The image can float on a page.
• ICR/ OCR technology uses registration mark on the four-corners of a
document, in the recognition of an image. Respondents place one
character per box on this form.
• The use of drop color reduces the size of the scanner’s output and
enhances the accuracy.
• Storage/ retrieval
• If the document needs to be electronically stored and maintained, then
OCR/ ICR is needed.
• OCR/ICR technologies, images can be scanned, indexed, and written to
optical media.
8. Advantages and Disadvantages
• Advantages of Using Images Rather Than Paper
• Quicker processing; no moving or storage of questionnaires near
operators
• Savings in costs and efficiencies by not having the paper questionnaires
• Scanning and recognition allowed efficient management and planning for
the rest of the processing workload
• Reduced long term storage requirements, questionnaires could be
destroyed after the initial scanning, recognition and repair
• Quick retrieval for editing and reprocessing
• Minimizes errors associated with physical handling of the questionnaires
9. Disadvantages of Using Images Rather Than Paper
• Accuracy
• While OCR technology can be effective in converting
handwritten or typed characters, it does not give as high
accuracy as of OMR for reading data, where users are
actually marking forms
• Additional workload to data collectors OCR has severe
limitations when it comes to human handwriting
• Characters must be hand-printed with separate
characters in boxes
10. OCR Systems
OCR systems consist of four major stages :
• Pre-processing
• Segmentation
• Feature Extraction
• Classification
• Post-processing
11. Pre-processing
The raw data is subjected to a number of preliminary processing steps to
make it usable in the descriptive stages of character analysis. Pre-
processing aims to produce data that are easy for the OCR systems to
operate accurately. The main objectives of pre-processing are :
• Binarization
• Noise reduction
• Stroke width normalization
• Skew correction
• Slant removal
12. Binarization
Document image binarization (thresholding) refers to the
conversion of a gray-scale image into a binary image. Two
categories of thresholding:
• Global, picks one threshold value for the entire document
image which is often based on an estimation of the
background level from the intensity histogram of the
image.
• Adaptive (local), uses different values for each pixel
according to the local area information
13. Noise Reduction - Normalization
Normalization provides a tremendous reduction in data size, thinning
extracts the shape information of the characters.
Noise reduction improves the quality of the document.
Two main approaches:
• Filtering (masks)
• Morphological Operations (erosion, dilation, etc)
14. Skew Correction
Skew Correction methods are used to align the paper document with the
coordinate system of the scanner. Main approaches for skew detection
include correlation, projection profiles, Hough transform.
15. Slant Removal
The slant of handwritten texts varies from user to user. Slant
removal methods are used to normalize the all characters to a
standard form.
Popular deslanting techniques are:
• Calculation of the average angle of near-vertical elements
• Bozinovic – Shrihari Method (BSM).
16. Slant Removal
Entropy
• The dominant slope of the character is found from the slope corrected
characters which gives the minimum entropy of a vertical projection
histogram. The vertical histogram projection is calculated for a range of
angles ± R. In our case R=60, seems to cover all writing styles. The slope
of the character, ,is found from:
m
a
H
R
a
m
min
i
N
i
i p
p
H log
1
• The character is then corrected by using: m
a
)
tan( m
a
y
x
x
y
y
17. Segmentation
Text Line Detection (Hough Transform, projections,
smearing)
Word Extraction (vertical projections, connected component
analysis)
Word Extraction 2 (RLSA)
18. Segmentation
Explicit Segmentation
In explicit approaches one tries to identify the smallest
possible word segments (primitive segments) that may be
smaller than letters, but surely cannot be segmented further.
Later in the recognition process these primitive segments are
assembled into letters based on input from the character
recognizer. The advantage of the first strategy is that it is
robust and quite straightforward, but is not very flexible.
Implicit Segmentation
In implicit approaches the words are recognized entirely
without segmenting them into letters. This is most effective
and viable only when the set of possible words is small and
known in advance, such as the recognition of bank checks
and postal address
19. Statistical Features
Representation of a character image by statistical distribution of
points takes care of style variations to some extent.
The major statistical features used for character representation are:
• Zoning
• Projections and profiles
• Crossings and distances
20. Zoning
The character image is divided into NxM zones. From each zone
features are extracted to form the feature vector. The goal of zoning is to
obtain the local characteristics instead of global characteristics
21. Zoning – Density Features
The number of foreground pixels, or the normalized number of
foreground pixels, in each cell is considered a feature.
Darker squares indicate higher density of zone pixels.
22. Zoning – Direction Features
Based on the contour of the character image
For each zone the contour is followed and a directional
histogram is obtained by analyzing the adjacent pixels in a
3x3 neighborhood
23. Zoning – Direction Features
Based on the skeleton of the character image
Distinguish individual line segments
Labeling line segment information
• Line segments are coded with a direction number
2 = vertical line segment
3 = right diagonal line segment
4 = horizontal line segment
5 = left diagonal line segment
Line type normalization
Formation of feature vector through zoning
number of
horizontal
lines
total length
of horizontal
lines
number
of
right
diagonal
lines
total length
of right
diagonal
lines
number
of
vertical
lines
total
length of
vertical
lines
number
of
left
diagonal
lines
total length
of left
diagonal
lines
number of
intersection points
24. Projection Histograms
The basic idea behind using projections is that character images, which are
2-D signals, can be represented as 1-D signal. These features, although
independent to noise and deformation, depend on rotation.
Projection histograms count the number of pixels in each
column and row of a character image. Projection histograms
can separate characters such as “m” and “n” .
25. Profiles
The profile counts the number of pixels (distance) between the bounding
box of the character image and the edge of the character. The profiles
describe well the external shapes of characters and allow to distinguish
between a great number of letters, such as “p” and “q”.
26. Profiles
Profiles can also be used to the contour of the character image
• Extract the contour of the character
• Locate the uppermost and the lowermost points of the
contour
• Calculate the in and out profiles of the contour
27. Crossings and Distances
Crossings count the number of transitions from background to foreground
pixels along vertical and horizontal lines through the character image and
Distances calculate the distances of the first image pixel detected from the
upper and lower boundaries, of the image, along vertical lines and from the
left and right boundaries along horizontal lines
28. Structural Features
Characters can be represented by structural features with high
tolerance to distortions and style variations. This type of
representation may also encode some knowledge about the
structure of the object or may provide some knowledge as to what
sort of components make up that object.
Structural features are based on topological and geometrical
properties of the character, such as aspect ratio, cross points, loops,
branch points, strokes and their directions, inflection between two
points, horizontal curves at top or bottom, etc.
30. Structural Features
A structural feature extraction method for recognizing Greek handwritten
characters [Kavallieratou et.al 2002]
Three types of features:
• Horizontal and Vertical projection histograms
• Radial histogram
• Radial out-in and radial in-out profiles
31. Global Transformations - Moments
The Fourier Transform (FT) of the contour of the image is
calculated. Since the first n coefficients of the FT can be used in order
to reconstruct the contour, then these n coefficients are considered to
be a n-dimesional feature vector that represents the character.
Central, Zenrike moments that make the process of
recognizing an object scale, translation, and rotation invariant.
The original image can be completely reconstructed from the
moment coefficients.
32. Classification
k-Nearest Neighbour (k-NN) , Bayes Classifier,
Neural Networks (NN), Hidden Markov Models
(HMM), Support Vector Machines (SVM), etc
There is no such thing as the “best classifier”. The use
of classifier depends on many factors, such as
available training set, number of free parameters etc.