On-line recognition of handwritten chinese characters based on hidden markov models

摘要

Difficulties in Chinese character recognition due to numerous strokes usually warped into a cursive form and a much larger set of characters. In this paper, we propose a hidden Markov model (HMM) based recognition model that deals efficiently with these recognition problems. The model is an interconnection network of radical and ligature HMMs. It works well with variations of the cursive strokes by the characteristics of the HMMs. It represents the large character set with a relatively small memory and also has good extensibility. To solve the problem of recognition speed caused by a number of search paths, we combine a modified level building search with the isolated radical and ligature HMMs in an attempt to achieve a robust, accurate recognizer whose performance is optimized. The algorithm is an efficient network search procedure, the time complexity of which depends on the number of levels in the network. A test with 18,000 handwritten characters shows a recognition rate of 90.3% and a speed of 1.83 s per character.