Automatic detection of OPCCR errors based on mesh grid density features of strokes
Wen Li · Journal of Sichuan University · 2011
At present,although the recognition rate of optical printed Chinese character recognition (OPCCR) systems has reached high up to 98 percents,there still exist recognition errors.Normally, the errors can not be detected automatically,but manually.Hence,it wastes time and labor significantly and reduces the extent of automation and intelligence of the systems.For this reason,an algorithm of automatic detection of OPCCR errors based on mesh grid density features of strokes has been proposed in this paper.First,a database of mesh grid density features for standard Chinese characters is built up. Then,to detect OPCCR errors,the image of optical printed Chinese characters(OPCC) is prepro-cessed, line cut and character cut to obtain individual characters.The mesh grid density features of the individual characters are extracted.The features are matched by correlation with the mesh grid density features in the feature database of the corresponding Chinese characters recognized.Thus,the OPCCR errors are detected according to the feature matching.