Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM

A holistic system for the recognition of handwritten Farsi/Arabic words using right–left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is us...

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Bibliographic Details
Published in:Pattern recognition 2001-05, Vol.34 (5), p.1057-1065
Main Authors: Dehghan, M., Faez, K., Ahmadi, M., Shridhar, M.
Format: Article
Language:English
Subjects:
Farsi/Arabic handwriting recognition
Handwritten word recognition
Hidden Markov model
Parameter smoothing
Self-organizing feature map
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Summary:A holistic system for the recognition of handwritten Farsi/Arabic words using right–left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The neighborhood information preserved in the self-organizing feature map (SOFM), is used for smoothing the observation probability distributions of trained HMMs. Experiments carried out on test samples show promising performance results.
ISSN:0031-3203
1873-5142
DOI:10.1016/S0031-3203(00)00051-0