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Guide to OCR for Arabic scripts

Author: Volker Märgner; Haikal El Abed
Publisher: London ; New York : Springer, ©2012.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Optical Character Recognition (OCR) is a key technology enabling access to digital text data. This technique is especially valuable for Arabic scripts, for which there has been very little digital access. Arabic script is widely used today. It is estimated that approximately 200 million people use Arabic as a first language, and the Arabic script is shared by an additional 13 languages, making it the second most  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Märgner, Volker.
Guide to OCR for Arabic Scripts.
Dordrecht : Springer, ©2012
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Volker Märgner; Haikal El Abed
ISBN: 9781447140726 1447140729
OCLC Number: 798921486
Language Note: English.
Description: 1 online resource
Contents: Part 1. Pre-Processing --
An Assessment of Arabic Handwriting Recognition Technology / Sargur N. Srihari and Gregory Ball --
Layout Analysis of Arabic Script Documents / Syed Saqib Bukhari, Faisal Shafait and Thomas M. Breuel --
A Multi-stage Approach to Arabic Document Analysis / Eugene Borovikov and Ilya Zavorin --
Pre-processing Issues in Arabic OCR / Zhixin Shi, Srirangaraj Setlur and Venu Govindaraju --
Segmentation of Ancient Arabic Documents / Abdel Belaïd and Nazih Ouwayed --
Features for HMM-Based Arabic Handwritten Word Recognition Systems / Laurence Likforman-Sulem, Ramy Al Hajj Mohammad, Chafic Mokbel, Fares Menasri and Anne-Laure Bianne-Bernard, et al. Part 2. Recognition --
Printed Arabic Text Recognition / Irfan Ahmed, Sabri A. Mahmoud and Mohammed Tanvir Parvez --
Handwritten Arabic Word Recognition Using the IFN/ENIT-database / Mario Pechwitz, Haikal El Abed and Volker Märgner --
RWTH OCR: A Large Vocabulary Optical Character Recognition System for Arabic Scripts / Philippe Dreuw, David Rybach, Georg Heigold and Hermann Ney --
Arabic Handwriting Recognition Using Bernoulli HMMs / Ihab Alkhoury, Adrià Giménez and Alfons Juan --
Handwritten Farsi Word Recognition Using Hidden Markov Models / Puntis Jifroodian Haghighi and Ching Y. Suen --
Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks / Alex Graves --
Application of Fractal Theory in Farsi/Arabic Document Analysis / Saeed Mozaffari --
Multi-stream Markov Models for Arabic Handwriting Recognition / Yousri Kessentini, Thierry Paquet and AbdelMajid Ben Hamadou --
Toward Distributed Cursive Writing OCR Systems Based on a Combination of Complementary Approaches / Maher Khemakhem and Abdelfettah Belghith. Part 3. Evaluation --
Data Collection and Annotation for Arabic Document Analysis / Ilya Zavorin and Eugene Borovikov --
Arabic Handwriting Recognition Competitions / Volker Märgner and Haikal El Abed --
Benchmarking Strategy for Arabic Screen-Rendered Word Recognition / Fouad Slimane, Slim Kanoun, Jean Hennebert, Rolf Ingold and Adel M. Alimi. Part 4. Applications --
A Robust Word Spotting System for Historical Arabic Manuscripts / Mohamed Cheriet and Reza Farrahi Moghaddam --
Arabic Text Recognition Using a Script-Independent Methodology: A Unified HMM-Based Approach for Machine-Printed and Handwritten Text / Premkumar Natarajan, Rohit Prasad, Huaigu Cao, Krishna Subramanian and Shirin Saleem, et al. --
Arabic Handwriting Recognition Using VDHMM and Over-segmentation / Amlan Kundu and Tom Hines --
Online Arabic Databases and Applications / Houcine Boubaker, Abdelkarim Elbaati, Najiba Tagougui, Haikal El Abed and Monji Kherallah, et al. --
On-line Arabic Handwritten Word Recognition Based on HMM and Combination of On-line and Off-line Features / Sherif Abdelazeem, Hesham M. Eraqi and Hany Ahmed.
Responsibility: Volker Märgner, Haikal El Abed, editors.

Abstract:

This detailed overview of Arabic character recognition technology covers pre-processing and feature extraction; HMM-based methods; evaluation of OCR systems; and applications of recognition  Read more...

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