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

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Guide to OCR for Arabic Scripts

Abstract

We present a novel large vocabulary OCR system, which implements a confidence- and margin-based discriminative training approach for model adaptation of an HMM-based recognition system to handle multiple fonts, different handwriting styles, and their variations. Most current HMM approaches are HTK-based systems which are maximum likelihood (ML) trained and which try to adapt their models to different writing styles using writer adaptive training, unsupervised clustering, or additional writer-specific data. Here, discriminative training based on the maximum mutual information (MMI) and minimum phone error (MPE) criteria are used instead. For model adaptation during decoding, an unsupervised confidence-based discriminative training within a two-pass decoding process is proposed. Additionally, we use neural network-based features extracted by a hierarchical multi-layer perceptron (MLP) network either in a hybrid MLP/HMM approach or to discriminatively retrain a Gaussian HMM system in a tandem approach. The proposed framework and methods are evaluated for closed-vocabulary isolated handwritten word recognition on the IFN/ENIT-database Arabic handwriting database, where the word error rate is decreased by more than 50 % relative to an ML trained baseline system. Preliminary results for large vocabulary Arabic machine-printed text recognition tasks are presented on a novel publicly available newspaper database.

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Notes

  1. 1.

    http://code.google.com/p/ocropus/

  2. 2.

    http://code.google.com/p/tesseract-ocr/

  3. 3.

    http://www.irislink.com/readiris/

  4. 4.

    http://www.novodynamics.com/

  5. 5.

    http://www.hltpr.rwth-aachen.de/rwth-ocr/

  6. 6.

    http://www.hltpr.rwth-aachen.de/rwth-asr/

  7. 7.

    http://www.ashrafraouf.com/mmac

  8. 8.

    http://www.hltpr.rwth-aachen.de/~dreuw/arabic.php

  9. 9.

    http://www.pdflib.com/products/tet/

  10. 10.

    http://www.layoutltd.com/arabicxt.php

  11. 11.

    http://code.google.com/p/ocropus/

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Acknowledgements

We would like to thank Christian Plahl, Stefan Hahn, Simon Wiesler, Patrick Doetsch, Robert Pyttel, Stephan Jonas, Jens Forster, Christian Gollan, and Thomas Deselaers for their support.

This work was partly realized as part of the Google Research Award “Robust Recognition of Machine Printed Text” and as part of the Quaero Programme, funded by OSEO, the French State agency for innovation.

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  1. Human Language Technology and Pattern Recognition, RWTH Aachen University, Ahornstr 55, 52056, Aachen, Germany

    Philippe Dreuw, David Rybach, Georg Heigold & Hermann Ney

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  1. Philippe Dreuw
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  1. Institute for Communications Technology, Braunschweig Technical University, Schleinitzstraße 22, Braunschweig, 38106, Niedersachsen, Germany

    Volker Märgner

  2. Institute for Communications Technology, Braunschweig Technical University, Schleinitzstraße 22, Braunschweig, 38106, Niedersachsen, Germany

    Haikal El Abed

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Dreuw, P., Rybach, D., Heigold, G., Ney, H. (2012). RWTH OCR: A Large Vocabulary Optical Character Recognition System for Arabic Scripts. In: Märgner, V., El Abed, H. (eds) Guide to OCR for Arabic Scripts. Springer, London. https://doi.org/10.1007/978-1-4471-4072-6_9

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