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From characters to words: dynamical segmentation and predictive neural networks

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5 Author(s)
Garcia-Salicetti, S. ; Dept. EPH, Inst. Nat. des Telecommun., Evry, France ; Gallinari, P. ; Dorizzi, B. ; Wimmer, Z.
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We present the extension of a neural predictive system primitively designed for on-line character recognition to words. Feature extraction is performed after resampling the pen trajectory information, recorded by a digitizing tablet. Each word is modeled by the natural concatenation of letter-models corresponding to the letters composing it. Successive parts of a word trajectory are this way modeled by different neural networks and only transitions from each one to itself or to its right neighbors are permitted. A holistic and dynamical segmentation allows one to adjust letter-models to the great variability of handwriting encountered in the words. Our system combines multilayer neural networks and dynamic programming with an underlying left-right hidden Markov model (HMM). Training was performed on 7000 words from 9 writers, leading to good results in the letter-labelling process, without using any language model

Published in:

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on  (Volume:6 )

Date of Conference:

7-10 May 1996

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