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Multi-lingual Offline Handwriting Recognition Using Hidden Markov Models: A Script-Independent Approach

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Arabic and Chinese Handwriting Recognition (SACH 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4768))

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Abstract

This paper introduces a script-independent methodology for multi-lingual offline handwriting recognition (OHR) based on the use of Hidden Markov Models (HMM). The OHR methodology extends our script-independent approach for OCR of machine-printed text images. The feature extraction, training, and recognition components of the system are all designed to be script independent. The HMM training and recognition components are based on our Byblos continuous speech recognition system. The HMM parameters are estimated automatically from the training data, without the need for laborious hand-written rules. The system does not require pre-segmentation of the data, neither at the word level nor at the character level. Thus, the system can handle languages with cursive handwritten scripts in a straightforward manner. The script independence of the system is demonstrated with experimental results in three scripts that exhibit significant differences in glyph characteristics: English, Chinese, and Arabic. Results from an initial set of experiments are presented to demonstrate the viability of the proposed methodology.

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Authors and Affiliations

  1. BBN Technologies, 10 Moulton Street, Cambridge, MA 02138, USA

    Prem Natarajan, Shirin Saleem, Rohit Prasad, Ehry MacRostie & Krishna Subramanian

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  1. Prem Natarajan
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  2. Shirin Saleem
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  3. Rohit Prasad
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  4. Ehry MacRostie
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  5. Krishna Subramanian
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David Doermann Stefan Jaeger

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Natarajan, P., Saleem, S., Prasad, R., MacRostie, E., Subramanian, K. (2008). Multi-lingual Offline Handwriting Recognition Using Hidden Markov Models: A Script-Independent Approach. In: Doermann, D., Jaeger, S. (eds) Arabic and Chinese Handwriting Recognition. SACH 2006. Lecture Notes in Computer Science, vol 4768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78199-8_14

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  • DOI: https://doi.org/10.1007/978-3-540-78199-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78198-1

  • Online ISBN: 978-3-540-78199-8

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