A hidden Markov model based segmentation and recognition algorithm for Chinese handwritten address character strings

An efficient method of Chinese handwritten address character string segmentation and recognition is presented. First, an address string image is presegmented into several radicals using stroke extraction and stroke mergence. Next, the radical series obtained by presegmentation merge into different character image series according to different merging paths. After that, the optimal merging path is selected using recognition and semantic information. The recognition information is given by the character classifier. The semantic information is obtained from large scale address database containing more than one hundred thousand address items. Finally, the optimal recognition results of the character image series which are combined by radical series according to the optimal merging paths are obtained. In experiments on 897 mail images, the proposed method achieves correct rate of 85 percent while the error rate is 15 percent.