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Offline cursive handwriting recognition system based on hybrid Markov model and neural networks

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4 Author(s)
Tay, Y.H. ; Center for Artificial Intelligence & Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia ; Khalid, M. ; Yusof, R. ; Viard-Gaudin, C.

An offline cursive handwriting recognition system based on hybrid of neural networks (NN) and hidden Markov models (HMM) is described in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segmentation to the recognition stage by generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each segmentation candidates (SCs) in the segmentation graph. Then, using concatenated letter-HMMs, a likelihood is computed for each word in the lexicon by multiplying the probabilities over the best paths through the graph. We present in detail two approaches to train the word recognizer: 1.) character-level training 2.) word-level training. The recognition performances of the two systems are discussed.

Published in:

Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on  (Volume:3 )

Date of Conference:

16-20 July 2003