Using neural network classifier in post-processing system for Handwritten Chinese Character Recognition

A new post-processing system for Handwritten Chinese Character Recognition System based on a neural networks classifier is presented. The recognition results for input character images, namely candidate characters and their confidence scores, as the observed features of the recognizer are classified into the most probable characters. The confusing character set is established by analyzing the large-scale recognition experimental results, and the statistical characteristics for a recognizer, are expressed as confusing character sets. 3755 character categories in GB2312-80 character-set are clustered into several hundreds of groups through searching the transitive closure of the similarity matrix associated with the confusing characters of each character category. A group of neural networks for these categories groups is established and trained to be a classifier in the post-processing to recover the unrecognized characters and adjust confidence scores of the candidate characters when a candidate sequence for each individual character image is given. The experimental results show that an average accuracy rate improvement of 5.6% and 3.8% for an online and an offline Handwritten Chinese Character Recognition system are achieved respectively.