Handwriting segmentation of Arabic text
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Segment confidence-based binary segmentation (SCBS) for cursive handwritten words
Expert Systems with Applications: An International Journal
Segmentation of connected handwritten digits using Self-Organizing Maps
Expert Systems with Applications: An International Journal
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This paper addresses an important and vital problem within the general area of character recognition, namely separating connected digits in handwritten numerals. The basic idea is employing multiagents to handle this problem. Our approach is mainly based on detecting the deepest-top valley and the highest-bottom hill, with one agent dedicated to each. The former agent decides on candidate cut-point as the closest feature point to the center of the deepest top-valley, if any. On the other hand, the other agent argues candidate cut-point as the closest feature point to the center of the highest bottom-hill, if any. After each of the two agents applies its own rules and reports a candidate cut-point, the two agents negotiate to agree on the actual cut-point. It may happen that both agents come to the negotiation with the same candidate cut-point. However, in general, each agent may find a different candidate cut-point and they negotiate a compromise on the actual cut-point, which may be neither of the two already proposed cut-points. A degree of confidence in each candidate cut-point influences the negotiation process. Experiments conducted so far are promising and successful. The obtained results are very encouraging with a success factor of 97.8%. Finally, none of the two agents alone achieved a close success rate.