Incorporation Of Relational Information In Feature Representation For Online Handwriting Recognition Of Arabic Characters
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Incorporation Of Relational Information In Feature Representation For Online Handwriting Recognition Of Arabic Characters
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Author : Sara Izadi Nia
language : en
Publisher:
Release Date : 2010
Incorporation Of Relational Information In Feature Representation For Online Handwriting Recognition Of Arabic Characters written by Sara Izadi Nia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Interest in online handwriting recognition is increasing due to market demand for both improved performance and for extended supporting scripts for digital devices. Robust handwriting recognition of complex patterns of arbitrary scale, orientation and location is elusive to date because reaching a target recognition rate is not trivial for most of the applications in this field. Cursive scripts such as Arabic and Persian with complex character shapes make the recognition task even more difficult. Challenges in the discrimination capability of handwriting recognition systems depend heavily on the effectiveness of the features used to represent the data, the types of classifiers deployed and inclusive databases used for learning and recognition which cover variations in writing styles that introduce natural deformations in character shapes. This thesis aims to improve the efficiency of online recognition systems for Persian and Arabic characters by presenting new formal feature representations, algorithms, and a comprehensive database for online Arabic characters. The thesis contains the development of the first public collection of online handwritten data for the Arabic complete-shape character set. New ideas for incorporating relational information in a feature representation for this type of data are presented. The proposed techniques are computationally efficient and provide compact, yet representative, feature vectors. For the first time, a hybrid classifier is used for recognition of online Arabic complete-shape characters based on the idea of decomposing the input data into variables representing factors of the complete-shape characters and the combined use of the Bayesian network inference and support vector machines. We advocate the usefulness and practicality of the features and recognition methods with respect to the recognition of conventional metrics, such as accuracy and timeliness, as well as unconventional metrics. In particular, we evaluate a feature representation for different character class instances by its level of separation in the feature space. Our evaluation results for the available databases and for our own database of the characters' main shapes confirm a higher efficiency than previously reported techniques with respect to all metrics analyzed. For the complete-shape characters, our techniques resulted in a unique recognition efficiency comparable with the state-of-the-art results for main shape characters.
Arabic And Chinese Handwriting Recognition
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Author : David Scott Doermann
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-04-03
Arabic And Chinese Handwriting Recognition written by David Scott Doermann and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-03 with Computers categories.
The book constitutes the refereed proceedings of the Summit on Arabic and Chinese Handwriting Recognition, SACH 2006, held in College Park, USA, September 27-28, 2006. The 16 revised full papers presented were carefully reviewed and selected from a total of over 60 submissions. The first six papers deal directly with Arabic handwriting together with a short historic survey of the language and techniques used in recognition. Five papers present the current research in Chinese handwriting and three more papers deal with cross cutting methods applied to other languages. The book closes with two articles on recognition of English and south Indian handwriting.
Guide To Ocr For Arabic Scripts
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Author : Volker Märgner
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-03
Guide To Ocr For Arabic Scripts written by Volker Märgner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-03 with Computers categories.
This Guide to OCR for Arabic Scripts is the first book of its kind, specifically devoted to this emerging field. Topics and features: contains contributions from the leading researchers in the field; with a Foreword by Professor Bente Maegaard of the University of Copenhagen; presents a detailed overview of Arabic character recognition technology, covering a range of different aspects of pre-processing and feature extraction; reviews a broad selection of varying approaches, including HMM-based methods and a recognition system based on multidimensional recurrent neural networks; examines the evaluation of Arabic script recognition systems, discussing data collection and annotation, benchmarking strategies, and handwriting recognition competitions; describes numerous applications of Arabic script recognition technology, from historical Arabic manuscripts to online Arabic recognition.
Towards An Arabic Handwritten Recognition System Using Machine Learning Model
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Author : Hassan Althobiati
language : en
Publisher:
Release Date : 2020
Towards An Arabic Handwritten Recognition System Using Machine Learning Model written by Hassan Althobiati and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Optical Character Recognition (OCR) is the process of identifying text in an image or a scanned document and convert it into a digital form. OCR systems have been used widely to convert images that have written content either printed or handwritten. Not only do they make viewing and sharing scanned documents easier, but they also allow searching through documents or images' content in a fast and successful manner. Over the course, a great number of approaches and techniques have been attempted to maximize the performance and reduce the error rate in multi-language OCR systems. However, Arabic language's recognition system still has some challenges and difficulties. The output of an Arabic Optical Character Recognition (AOCR) system is usually vague and unpredictable due to that fact that most Arabic characters are considered multi-object characters. Moreover, many systems and applications do not fully support Arabic language. Therefore, efforts and hard work should be done to implement a system that is able to fully support Arabic language, especially Arabic handwritten characters. Even though research shows good results of machine-printed texts, Arabic handwritten recognition still needs improvements and amendments. To avert the unreliability of handwritten recognition systems, three research studies were conducted. In addition, a unique dataset is built specifically to train and test the efficiency and reliability of the systems. First, a survey research is conducted to understand what the AOCR field has, and what have been done so far. After obtaining the required knowledge, the first Arabic Handwritten Optical Character Recognition (AHOCR) system is developed based on a boundary descriptor algorithm called Freeman chain code. The Freeman chain code is an external representation used to describe an object based on its boundaries. The well-known chain code was chosen because Arabic characters are written in a cursive style and Freeman chain code can provide some crucial information that can be used to measure the curviness of a character, such as perimeter and circularity. Because some Arabic characters have diacritics and dots, a bounding box, which is the smallest box containing a character, is used to capture all the small parts of a character and group them into one small glyph. It should be noted that the bounding box is the smallest possible box containing a character, including holes, dots and symbols. This method is responsible for dividing the entire image into small pieces called "glyphs". The system can recognize most Arabic characters, yet some cursive characters, where multiple cursive lines overlapped, were not identified correctly. Therefore, another AHOCR system is developed, and it is based on Freeman chain code and Tangent Line & Change in Tangent. The main aim of using this method is to measure the rate of change of a cursive line and divide the character into sub-objects. Therefore, the number of sub-objects determines the correct class of the character being checked. By using Tangent Line, the system was able to recognize curvy characters that cannot be recognized using the previous method of encoded Freeman chain code. It has been determined that the method of Tangent Line & Change in Tangent can correct most misclassifications that happened because of using Freeman chain code method. The need for a fast and reliable AHOCR system arose when the previous systems misclassified some characters. Also, calculating the Freeman chain code and measuring the change of rate is time consuming. Therefore, a third AHOCR system that uses machine learning model is developed based on Support Vector Machine (SVM). The system uses Normalized Central Moments (NCM) as well as Local Binary Patterns (LBP) as feature extraction to recognize isolated Arabic handwritten characters. Furthermore, the bounding box is also used in the pre-recognition stage to find the main body of each character along with any auxiliary parts that are associated with it. The proposed algorithm has proved that not only has a higher recognition rate, but also performs better when combining multiple features. Even though there were some difficulties when identifying similar characters, the overall recognition system appeared to be good enough comparing to the difficulties of Arabic language in general. This dissertation represents an extensive survey of Arabic language characters and its difficulties. Further, three AHOCR systems with different algorithms are described in detail to accurately identify isolated Arabic handwritten characters. It also provides a detailed explanation of the experimental results and discusses the limitations and future work
Feature Based Arabic Handwriting Recognition For Teaching Illiterates
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Author : Mohammad Amin Abou Harb
language : en
Publisher:
Release Date : 2011
Feature Based Arabic Handwriting Recognition For Teaching Illiterates written by Mohammad Amin Abou Harb and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Handbook Of Character Recognition And Document Image Analysis
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Author : Horst Bunke
language : en
Publisher: World Scientific
Release Date : 1997-05-02
Handbook Of Character Recognition And Document Image Analysis written by Horst Bunke and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-05-02 with Computers categories.
Optical character recognition and document image analysis have become very important areas with a fast growing number of researchers in the field. This comprehensive handbook with contributions by eminent experts, presents both the theoretical and practical aspects at an introductory level wherever possible.
Pattern Recognition Statistical Structural And Neural Approaches
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Author : Schalkoff
language : en
Publisher: John Wiley & Sons
Release Date : 2007-09
Pattern Recognition Statistical Structural And Neural Approaches written by Schalkoff and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09 with categories.
About The Book: This book explores the heart of pattern recognition concepts, methods and applications using statistical, syntactic and neural approaches. Divided into four sections, it clearly demonstrates the similarities and differences among the three approaches. The second part deals with the statistical pattern recognition approach, starting with a simple example and finishing with unsupervised learning through clustering. Section three discusses the syntactic approach and explores such topics as the capabilities of string grammars and parsing; higher dimensional representations and graphical approaches. Part four presents an excellent overview of the emerging neural approach including an examination of pattern associations and feedforward nets. Along with examples, each chapter provides the reader with pertinent literature for a more in-depth study of specific topics.
A Large Vocabulary Online Handwriting Recognition System For Turkish
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Author : Esma Fatıma Bilgin Taşdemir
language : en
Publisher: Cinius Yayınları
Release Date : 2021-12-01
A Large Vocabulary Online Handwriting Recognition System For Turkish written by Esma Fatıma Bilgin Taşdemir and has been published by Cinius Yayınları this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-01 with Fiction categories.
Handwriting recognition in general and online handwriting recognition in particular has been an active research area for several decades. Most of the research have been focused on English and recently on other scripts like Arabic and Chinese. There is a lack of research on recognition in Turkish text and this work primarily fills that gap with a state-of-the-art recognizer for the first time. It contains design and implementation details of a complete recognition system for recognition of Turkish isolated words. It considers the recognition of unconstrained handwriting with a limited vocabulary size first and then evolves to a large vocabulary system. Turkish script has many similarities with other Latin scripts, like English, which makes it possible to adapt strategies that work for them. However, there are some other issues which are particular to Turkish that should be taken into consideration separately. Two of the challenging issues in recognition of Turkish text are determined as delayed strokes and high Out-of-Vocabulary (OOV). This work examines these problems and alternative solutions at depth and proposes suitable solutions for Turkish script particularly.
Character Recognition Systems
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Author : Mohamed Cheriet
language : en
Publisher: John Wiley & Sons
Release Date : 2007-11-27
Character Recognition Systems written by Mohamed Cheriet and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-27 with Technology & Engineering categories.
"Much of pattern recognition theory and practice, including methods such as Support Vector Machines, has emerged in an attempt to solve the character recognition problem. This book is written by very well-known academics who have worked in the field for many years and have made significant and lasting contributions. The book will no doubt be of value to students and practitioners." -Sargur N. Srihari, SUNY Distinguished Professor, Department of Computer Science and Engineering, and Director, Center of Excellence for Document Analysis and Recognition (CEDAR), University at Buffalo, The State University of New York "The disciplines of optical character recognition and document image analysis have a history of more than forty years. In the last decade, the importance and popularity of these areas have grown enormously. Surprisingly, however, the field is not well covered by any textbook. This book has been written by prominent leaders in the field. It includes all important topics in optical character recognition and document analysis, and is written in a very coherent and comprehensive style. This book satisfies an urgent need. It is a volume the community has been awaiting for a long time, and I can enthusiastically recommend it to everybody working in the area." -Horst Bunke, Professor, Institute of Computer Science and Applied Mathematics (IAM), University of Bern, Switzerland In Character Recognition Systems, the authors provide practitioners and students with the fundamental principles and state-of-the-art computational methods of reading printed texts and handwritten materials. The information presented is analogous to the stages of a computer recognition system, helping readers master the theory and latest methodologies used in character recognition in a meaningful way. This book covers: * Perspectives on the history, applications, and evolution of Optical Character Recognition (OCR) * The most widely used pre-processing techniques, as well as methods for extracting character contours and skeletons * Evaluating extracted features, both structural and statistical * Modern classification methods that are successful in character recognition, including statistical methods, Artificial Neural Networks (ANN), Support Vector Machines (SVM), structural methods, and multi-classifier methods * An overview of word and string recognition methods and techniques * Case studies that illustrate practical applications, with descriptions of the methods and theories behind the experimental results Each chapter contains major steps and tricks to handle the tasks described at-hand. Researchers and graduate students in computer science and engineering will find this book useful for designing a concrete system in OCR technology, while practitioners will rely on it as a valuable resource for the latest advances and modern technologies that aren't covered elsewhere in a single book.
Introduction To Information Retrieval
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Author : Christopher D. Manning
language : en
Publisher: Cambridge University Press
Release Date : 2008-07-07
Introduction To Information Retrieval written by Christopher D. Manning and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-07 with Computers categories.
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.