Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Computer Recognition Of Printed And Hand Written Arabic Scripts
Download Computer Recognition Of Printed And Hand Written Arabic Scripts full books in PDF, epub, and Kindle. Read online Computer Recognition Of Printed And Hand Written Arabic Scripts ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Guide to OCR for Arabic Scripts by : Volker Märgner
Download or read book Guide to OCR for Arabic Scripts written by Volker Märgner and published by Springer Science & Business Media. This book was released on 2012-07-03 with total page 593 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Computer Recognition of Printed and Hand-written Arabic Scripts by : Shawki Dakduk
Download or read book Computer Recognition of Printed and Hand-written Arabic Scripts written by Shawki Dakduk and published by . This book was released on 1992 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Arabic and Chinese Handwriting Recognition by : David Doermann
Download or read book Arabic and Chinese Handwriting Recognition written by David Doermann and published by Springer. This book was released on 2008-03-13 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Cursive Script Text Recognition in Natural Scene Images by : Saad Bin Ahmed
Download or read book Cursive Script Text Recognition in Natural Scene Images written by Saad Bin Ahmed and published by Springer Nature. This book was released on 2019-11-21 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a broad and structured overview of the state-of-the-art methods that could be applied for context-dependent languages like Arabic. It also provides guidelines on how to deal with Arabic scene data that appeared in an uncontrolled environment impacted by different font size, font styles, image resolution, and opacity of text. Being an intrinsic script, Arabic and Arabic-like languages attract attention from research community. There are a number of challenges associated with the detection and recognition of Arabic text from natural images. This book discusses these challenges and open problems and also provides insights into the complexities and issues that researchers encounter in the context of Arabic or Arabic-like text recognition in natural and document images. It sheds light on fundamental questions, such as a) How the complexity of Arabic as a cursive scripts can be demonstrated b) What the structure of Arabic text is and how to consider the features from a given text and c) What guidelines should be followed to address the context learning ability of classifiers existing in machine learning.
Book Synopsis Arabic Hand Written Segmentation and Recognition by : K Dhamad Muna
Download or read book Arabic Hand Written Segmentation and Recognition written by K Dhamad Muna and published by LAP Lambert Academic Publishing. This book was released on 2015-01-06 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book illustrates the design an offline Arabic handwritten text recognition system using structural techniques and several proposed algorithms. The ultimate aim of handwriting recognition is to make computers able to understand human written texts, with a performance comparable to that of humans. The domain of handwriting in the Arabic script presents unique technical challenges. This is because a handwritten text offers great challenges such as character and word segmentation, character recognition, variation between handwriting styles, different character size.
Book Synopsis Arabic Handwriting Recognition Using Machine Learning Approaches by :
Download or read book Arabic Handwriting Recognition Using Machine Learning Approaches written by and published by . This book was released on 2007 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Handbook of Document Image Processing and Recognition by : David Doermann
Download or read book Handbook of Document Image Processing and Recognition written by David Doermann and published by Springer. This book was released on 2014-05-22 with total page 1055 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Document Image Processing and Recognition is a comprehensive resource on the latest methods and techniques in document image processing and recognition. Each chapter provides a clear overview of the topic followed by the state of the art of techniques used – including elements of comparison between them – along with supporting references to archival publications, for those interested in delving deeper into topics addressed. Rather than favor a particular approach, the text enables the reader to make an informed decision for their specific problems.
Book Synopsis Guide to OCR for Indic Scripts by : Venu Govindaraju
Download or read book Guide to OCR for Indic Scripts written by Venu Govindaraju and published by Springer Science & Business Media. This book was released on 2009-09-25 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive text on Optical Character Recognition for Indic scripts. It covers many topics and describes OCR systems for eight different scripts—Bangla, Devanagari, Gurmukhi, Gujarti, Kannada, Malayalam, Tamil and Urdu.
Book Synopsis Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications by : Ruben Vera-Rodriguez
Download or read book Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications written by Ruben Vera-Rodriguez and published by Springer. This book was released on 2019-03-02 with total page 1001 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, held in Madrid, Spain, in November 2018 The 112 papers presented were carefully reviewed and selected from 187 submissions The program was comprised of 6 oral sessions on the following topics: machine learning, computer vision, classification, biometrics and medical applications, and brain signals, and also on: text and character analysis, human interaction, and sentiment analysis
Book Synopsis 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR) by : IEEE Staff
Download or read book 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR) written by IEEE Staff and published by . This book was released on 2018-03-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: It is our pleasure to invite you to participate in the 2nd IEEE International Workshop on Arabic and derived Script Analysis and Recognition (ASAR 2018), which will be hosted by the Alan Turing Institute, London, in collaboration with the LORIA laboratory (University Lorraine, France) and REGIM Lab (University of Sfax, Tunisia), and will be held in London (United Kingdom) on March 12 14, 2018 The ASAR workshop provides an excellent opportunity for researchers and practitioners at all levels of experience to meet colleagues and to share new ideas and knowledge about Arabic and derived script document analysis and recognition methods The workshop enjoys strong participation from researchers in both industry and academia
Book Synopsis Arabic Handwritten Text Recognition and Writer Identification by : Mustafa S. Kadhm
Download or read book Arabic Handwritten Text Recognition and Writer Identification written by Mustafa S. Kadhm and published by . This book was released on 2017-11-06 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doctoral Thesis / Dissertation from the year 2017 in the subject Computer Science - Applied, grade: 3, language: English, abstract: Most of the governments and organizations have a huge number of handwritten documents generated by their daily processes. It is imperative to use computers to read the generated handwritten texts, and make them editable and searchable. Therefore, handwritten recognition lately became a very popular research topic and the number of its possible applications is very large. It's capable in resolving complex problems and simplify human activities by converting the handwritten documents into digital form. However, the Arabic handwritten text recognition is a complex process compared with other handwritten languages because Arabic handwritten text is cursive of nature. Therefore, this thesis proposed an Arabic handwritten text recognition and writer identification system based on segmenting the input handwritten text into handwritten sub-words. The system has two main modules that are used, for the recognition of the handwritten text and identifying the text's writer. The first module1 has six stages that work together to recognize the Arabic handwritten text and convert it into editable text. These stages are: image acquisition, segmentation, preprocessing, features base construction, classification and post-processing. The second module2 is identified the desired text's writers through several stages that similar to module1. The system proposes an efficient and accurate segmentation algorithm that segments the input handwritten text into a number of handwritten sub-images and each of these segmented sub-images has an Arabic handwritten sub-word. Besides that, an image thresholding algorithm is proposed to convert the sub-images into binary based on using fuzzy c-mean clustering method. Furthermore, the binary sub-images went through proposed noise removal algorithm in order to remove undesired pixels. After that, two groups of features ar
Book Synopsis Word Based Off-line Handwritten Arabic Classification and Recognition by : Jawad AlKhateeb
Download or read book Word Based Off-line Handwritten Arabic Classification and Recognition written by Jawad AlKhateeb and published by LAP Lambert Academic Publishing. This book was released on 2011-07 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Arabic script evolved from the Nabataean Aramaic script. It has been used since the 4th century AD, but the earliest document, an inscription in Arabic, Syriac and Greek, dates from 512 AD. The Aramaic language has fewer consonants than Arabic, so during the 7th century new Arabic letters were created by adding dots to existing letters in order to avoid ambiguities. Further diacritics indicating short vowels were introduced, but are only generally used to ensure the Qur'an was read aloud without mistakes.
Book Synopsis Towards an Arabic Handwritten Recognition System Using Machine Learning Model by : Hassan Althobiati
Download or read book Towards an Arabic Handwritten Recognition System Using Machine Learning Model written by Hassan Althobiati and published by . This book was released on 2020 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Handbook of Research on Machine Learning Innovations and Trends by : Aboul Ella Hassanien
Download or read book Handbook of Research on Machine Learning Innovations and Trends written by Aboul Ella Hassanien and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuous improvements in technological applications have allowed more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but it increases the overall probability of technological progression. The Handbook of Research on Machine Learning Innovations and Trends is a key resource on the latest advances and research regarding the vast range of advanced systems and applications involved in machine intelligence. Highlighting multidisciplinary studies on decision theory, intelligent search, and multi-agent systems, this publication is an ideal reference source for professionals and researchers working in the field of machine learning and its applications.
Book Synopsis Algorithms for Image Processing and Computer Vision by : J. R. Parker
Download or read book Algorithms for Image Processing and Computer Vision written by J. R. Parker and published by John Wiley & Sons. This book was released on 2010-11-29 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: A cookbook of algorithms for common image processing applications Thanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. It’s an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing. Algorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialists This bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aids Saves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications. Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.
Book Synopsis Hybrid Intelligent Systems by : Ajith Abraham
Download or read book Hybrid Intelligent Systems written by Ajith Abraham and published by Springer Nature. This book was released on 2020-08-12 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the recent research on hybrid intelligent systems and their various practical applications. It presents 34 selected papers from the 18th International Conference on Hybrid Intelligent Systems (HIS 2019) and 9 papers from the 15th International Conference on Information Assurance and Security (IAS 2019), which was held at VIT Bhopal University, India, from December 10 to 12, 2019. A premier conference in the field of artificial intelligence, HIS - IAS 2019 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from 20 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
Book Synopsis Handbook of Character Recognition and Document Image Analysis by : Horst Bunke
Download or read book Handbook of Character Recognition and Document Image Analysis written by Horst Bunke and published by World Scientific. This book was released on 1997 with total page 868 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
