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A handwritten Chinese characters recognition method based on primitive and fuzzy features via SEART neural net model

A handwritten Chinese characters recognition method using SEART neural network model with primitive and compound fuzzy features is proposed. The primitive features are extracted in local and global view. Also they have good stability. Since the writings of handwritten Chinese characters vary a lot, we adopt fuzzy concept to extract the compound features in structural view. These categories of features are extracted in one pass, so the computational effort is not heavy. We combine the two categories of features and use a fast classifier, named supervised extended ART (SEART) neural network model to recognize the handwritten Chinese characters. The SEART classifier has excellent performance, fast, good generalization and exceptions handling ability in complex problems. Using the fuzzy concept in features extraction and the neural network as a classifier are helpful for tolerating distortions, noises and variations. In spite of the poor thinning, an average of 90.24% recognition rate on the 605 test characters is obtained. The database used is HCCRBASE (provided by CCL, ITRI, Taiwan). It not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy concept and neural networks on HCCR is an efficient and promising approach.