Arabic Online Handwriting Recognition
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Arabic And Chinese Handwriting Recognition
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Author : David Doermann
language : en
Publisher: Springer
Release Date : 2008-03-13
Arabic And Chinese Handwriting Recognition written by David Doermann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-13 with Computers categories.
In the fall of 2006, the University of Maryland, along with various government and industrial sponsors, invited leading researchers from all over the world to a two-day Summit on Arabic and Chinese Handwriting Recognition (SACH 2006). The event acted as a complement to the biennial Symposium on Document Image Understanding Technology (SDIUT), providing a focused glimpse into the state of the art in Arabic and Chinese handwriting recognition. It offered a forum for interaction with prominent researchers at the forefront of the scientific community and provided an opportunity for participants to help explore possible directions of the field. This book is a result of the expansion, peer review, and revision of selected papers presented at this meeting. Handwriting recognition remains the Holy Grail of document analysis, and Arabic and Chinese scripts embrace many of the most significant challenges. We are pleased to have 16 scientific papers covering the original topics of handwritten Arabic and Chinese, as well as 2 papers covering other handwritten scripts. We asked each author to not only describe the techniques used in addressing the problem, but to attempt to identify the key research challenges and problems that the community faces. The result is an impressive collection of manuscripts that provide various detailed views of the state of research. In this book, six articles deal directly with Arabic handwriting. • Cheriet provides an overview of the problems of Arabic recognition and how systems can use natural language processing techniques to correct errors in lexicon-based systems.
Arabic Online Handwriting Recognition
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Author :
language : en
Publisher:
Release Date : 2008
Arabic Online Handwriting Recognition written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
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.
Online Arabic Handwriting Recognition Using Hidden Markov Models
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Author : Fadi Biadsy
language : en
Publisher:
Release Date : 2005
Online Arabic Handwriting Recognition Using Hidden Markov Models written by Fadi Biadsy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Manuscripts, Arabic categories.
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.
Online Video Based Arabic Handwriting Recognition System
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Author : Husamudin Hajjaj
language : en
Publisher:
Release Date : 2007
Online Video Based Arabic Handwriting Recognition System written by Husamudin Hajjaj and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Dissertations, Academic categories.
Robust Regression On Clustered Data And Signature Based Online Arabic Handwriting Recognition
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Author : Daniel Wilson-Nunn
language : en
Publisher:
Release Date : 2021
Robust Regression On Clustered Data And Signature Based Online Arabic Handwriting Recognition written by Daniel Wilson-Nunn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
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.
Arabic Handwriting Recognition Using Machine Learning Approaches
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Author :
language : en
Publisher:
Release Date : 2007
Arabic Handwriting Recognition Using Machine Learning Approaches written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
While handwriting recognition tasks for Latin script based languages have received considerable attention, far less work has been done on the Arabic script. Arabic poses some unique challenges, such as a larger character set, the presence of dots and diacritics, and intra-word whitespace regions. Machine learning approaches have the potential to significantly improve state of the art Arabic handwriting recognition results. This dissertation presents several such machine learning techniques, such as writer adaptation and segmentation free unconstrained text processing. We integrate these techniques into novel algorithms for general recognition, word spotting, and transcript mapping. Writer adaptation or specialization is the adjustment of handwriting recognition algorithms to a specific writer's style of handwriting. Such adjustment yields significantly improved recognition rates over a generalized recognition counterpart algorithms. Specialization is commonly used in online Latin script handwriting applications, such as for tablet computers or PDAs. Some rudimentary offline Latin script adaptation methods have been proposed recently in the literature as well. Handwriting adaptation for the Arabic script, however, is unexplored. An iterative bootstrapping model is presented which adapts a writer-independent model to a writer-dependent model using a small number of words achieving a large recognition rate increase in the process. Furthermore, a confidence weighting method is described which generates better results by weighting words based on their length. Script features unique to Arabic are discussed, as well as they are incorporated into the adaptation process. Even though Arabic has many more character classes than languages such as English, significant improvement is observed. One issue common to Arabic recognition tasks is generating candidate word regions on a page. Attempting to definitely segment the document into such regions (automatic segmentation) can meet with some success, but the performance of such an algorithm is often a limiting factor in spotting performance. Another approach is to directly scan the image on the page without attempting to generate such a definite segmentation. Such segmentation-free approaches result in better recognition at a performance cost. The algorithms discussed are tested using a database of truthed, page-length, handwritten Arabic documents. Where applicable, the literature standard IFN/ENIT database is used for testing as well. We validate our approaches by exploring the implications on such tasks as word spotting (attempting to find a query word or image and placement in a set of documents), transcript mapping (the automatic alignment of a handwritten document with its machine readable transcript), and general unconstrained recognition. Novel algorithms for these tasks are also presented. Specifically, contributions in this dissertation include novel descriptions of machine learning algorithms applied to Arabic handwriting recognition problems and quantification of the improvement generated by their usage. Examples of such algorithms include writer adaptation, versatile search, and the advantage and trade offs gained by processing such tasks as word spotting in a segmentation-free fashion instead of a segmentation-based manner.
Deep Learning Applications To Offline Arabic Handwriting Words Recognition Using Convolutional Neural Network
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Author : Nori Alzrrog
language : en
Publisher:
Release Date : 2023
Deep Learning Applications To Offline Arabic Handwriting Words Recognition Using Convolutional Neural Network written by Nori Alzrrog and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Automatic handwriting recognition is the process of converting online and offline letters or words as a graphical form into its text format. Automatic Arabic Handwriting words recognition using deep learning neural networks is still in the early stages in terms of research. There are no general, complete, and reliable Arabic Handwritten Words (AHW) database (lexicon) that can be used as a reference or a benchmark for all researchers who want to extend the work on automatic Arabic handwriting word recognition. Also, many historic Arabic manuscripts have deteriorated because of inappropriate storage and most of them have not been digitized due to the lack of reliable database that can be used to recognize the words of Arabic manuscripts. Deep Convolutional Neural Networks (DCNNs) can be used to solve the problems of automatic Arabic handwriting words recognition. In this work, a new DCNN algorithm applied to a new dataset of Handwritten Arabic words representing the seven days of the week named Arabic Handwritten Weekdays Dataset (AHWD) has been programmed, tested, and analyzed.