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On-Line Handwriting Word Recognition Using a Bi-character Model

This paper deals with on-line handwriting recognition. Analytic approaches have attracted an increasing interest during the last ten years. These approaches rely on a preliminary segmentation stage, which remains one of the most difficult problems and may affect strongly the quality of the global recognition process. In order to circumvent this problem, this paper introduces a bi-character model, where each character is recognized jointly with its neighboring characters. This model yields two main advantages. First, it reduces the number of confusions due to connections between characters during the character recognition step. Second, it avoids some possible confusion at the character recognition level during the word recognition stage. Our experimentation on significant databases shows some interesting improvements of the recognition rate, since the recognition rate is increased from 65% to 83% by using this bi-character strategy.