Handwritten Character Recognition Based on Weighted Integral Image and Probability Model

Jia Wu, Feipeng Da, Chenxing Wang, Shaoyan Gai

Abstract: A system of the off-line handwritten character recognition based on weighted integral image and probability model is built in this paper, which is divided into image preprocessing and character recognition. The objects of recognition are digitals and letters. In the image preprocessing section, an adaptive binarization method based on weighted integral image is proposed, which overcomes the drawbacks in the classic binarization algorithms: noise sensitivity, edge coarseness, artifacts etc.; In the character recognition section, combined with statistical features and structural features, an probability model based on the Bayes classifier and the principle of similar shapes is developed. This method achieves a high recognition rate with rapid processing, strong anti-interference ability and fault tolerance.