A NOVEL METHOD BASED ON INTEGRATING CHARACTERS WITH WORDS FOR CONTEXTUAL PROCESSING OF CHINESE CHARACTER RECOGNITION

According to the complementarity between Chinese characters and Chinese words, a novel contextual processing method is put forward, which integrates character based language model with word based language model. On the basis of isolated character recognition, character based bigram post processing using forward backward search is first executed on big candidate sets, which improves both the recognition rate of document （RRD） and the efficiency of candidate sets （the accumulated recognition rate of the top ten candidates is greatly boosted）. Then, word based bigram post processing is executed on small candidate sets to further improve the RRD. This method effectively improves the RRD while giving attention to the processing speed in the meantime. Experimental results on off line handwritten Chinese documents （about 66000 characters） demonstrate the effectiveness of the novel method: character based bigram post processing improves the RRD to 94.50% from 81.58% RRD before post processing, and the accumulated recognition rate of the top ten candidates boosts from 94.33% to 98.25%. The 95 75% RRD is obtained after word based bigram post processing.