Active radical modeling for handwritten Chinese characters

Handwritten Chinese character recognition is one of the most difficult problems of pattern recognition. Since the majority of Chinese characters are made up from just a small set of primitive structures (radicals), this paper describes an approach to active radical modeling for such handwritten characters. The most significant characteristic of our method is that radicals can be found robustly without stroke extraction, and the principal variations of the radical can be encoded in a small number of parameters. In the training phase, the example radicals are represented by manually-labeled ´landmark´ points. Then a small number of principal components of the eigenvectors are calculated to capture the main variation of the training examples from the mean radical. In the matching phase, each radical model is fitted to the image evidence by adjusting the shape parameters in terms of chamfer distance minimization. Initial experiments are conducted on 1,100 loosely-constrained Chinese character categories written by 200 different writers. The correct matching rate is 95.8%, showing that our radical modeling is effective and capable of forming a sound basis for handwritten Chinese character recognition