Handwritten Chinese character recognition based on supervised competitive learning neural network and block-based relative fuzzy feature extraction
Li-Min Sun, Shuanhu Wu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Offline handwritten chinese character recognition (HCCR) is still a difficult problem because of its large stroke changes, writing anomaly, and the difficulty for obtaining its stroke ranking information. Generally, offline HCCR can be divided into two procedures: feature extraction for capturing handwritten Chinese character information and feature classifying for character recognition. In this paper, we proposed a new chinese character recognition algorithm. In feature extraction part, we adopted elastic mesh dividing method for extracting the block features and its relative fuzzy features that utilized the relativities between different strokes and distribution probability of a stroke in its neighbor sub-blocks. In recognition part, we constructed a classifier based on a supervisory competitive learning algorithm to train competitive learning neural network with the extracted features set. Experimental results show that the performance of our algorithm is encouraging and can be comparable to other algorithms.