Guide to OCR for Arabic Scripts

1. Front Matter

   Pages I-XX

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2. Pre-processing

   1. Front Matter

      Pages 1-1

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3. Pre-Processing

   1. An Assessment of Arabic Handwriting Recognition Technology

      • Sargur N. Srihari, Gregory Ball

      Pages 3-34

   2. Layout Analysis of Arabic Script Documents

      • Syed Saqib Bukhari, Faisal Shafait, Thomas M. Breuel

      Pages 35-53

   3. A Multi-stage Approach to Arabic Document Analysis

      • Eugene Borovikov, Ilya Zavorin

      Pages 55-78

   4. Pre-processing Issues in Arabic OCR

      • Zhixin Shi, Srirangaraj Setlur, Venu Govindaraju

      Pages 79-102

   5. Segmentation of Ancient Arabic Documents

      • Abdel Belaïd, Nazih Ouwayed

      Pages 103-122

   6. Features for HMM-Based Arabic Handwritten Word Recognition Systems

      • Laurence Likforman-Sulem, Ramy Al Hajj Mohammad, Chafic Mokbel, Fares Menasri, Anne-Laure Bianne-Bernard, Christopher Kermorvant

      Pages 123-143

4. Recognition

   1. Front Matter

      Pages 145-145

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   2. Printed Arabic Text Recognition

      • Irfan Ahmed, Sabri A. Mahmoud, Mohammed Tanvir Parvez

      Pages 147-168

   3. Handwritten Arabic Word Recognition Using the IFN/ENIT-database

      • Mario Pechwitz, Haikal El Abed, Volker Märgner

      Pages 169-213

   4. RWTH OCR: A Large Vocabulary Optical Character Recognition System for Arabic Scripts

      • Philippe Dreuw, David Rybach, Georg Heigold, Hermann Ney

      Pages 215-254

   5. Arabic Handwriting Recognition Using Bernoulli HMMs

      • Ihab Alkhoury, Adrià Giménez, Alfons Juan

      Pages 255-272

   6. Handwritten Farsi Word Recognition Using Hidden Markov Models

      • Puntis Jifroodian Haghighi, Ching Y. Suen

      Pages 273-295

   7. Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks

      • Alex Graves

      Pages 297-313

   8. Application of Fractal Theory in Farsi/Arabic Document Analysis

      • Saeed Mozaffari

      Pages 315-333

   9. Multi-stream Markov Models for Arabic Handwriting Recognition

      • Yousri Kessentini, Thierry Paquet, AbdelMajid Ben Hamadou

      Pages 335-350

   10. Toward Distributed Cursive Writing OCR Systems Based on a Combination of Complementary Approaches

       • Maher Khemakhem, Abdelfettah Belghith

       Pages 351-371

5. Evaluation

   1. Front Matter

      Pages 373-373

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   2. Data Collection and Annotation for Arabic Document Analysis

      • Ilya Zavorin, Eugene Borovikov

      Pages 375-394

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.

• Document Analysis
• Hidden Markov Models
• Neural Networks
• Pattern Recognition
• Text/Word Recognition

• Institute for Communications Technology, Braunschweig Technical University, Braunschweig, Germany

  Volker Märgner, Haikal El Abed

Volker Märgner is Academic Director of the Institute for Communications Technology (IfN) at Technische Universität Braunschweig, Germany. He has over 30 years research experience in image processing, pattern recognition, and handwriting recognition. He developed the IfN/ENIT-database of Arabic handwritten names which is the reference for Arabic handwritten word recognition systems and organized competitions both together with Haikal El Abed.

Haikal El Abed is a Senior Research Engineer at the Institute for Communications Technology (IfN) at Technische Universität Braunschweig, Germany. He has more than 10 years research experience in pattern recognition and Arabic text recognition, on-line and off-line. He organizes competitions and works on the collection of databases.

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