Segmentation of knowledge-radicals for on-line handwritten Chinese characters
Chao‐Hao Lee, Ming‐Wen Chang, Hon-Fai Yau, Bor-Shenn Jeng, Dung-Ming Shieh, Char-Shin Miou, Chi‐Jain Wen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
In this paper, a segmentation method of knowledge-radicals for on-line handwritten Chinese characters (OLHCC) is proposed. Using the methods of finding local minimum and finding minimum of sum in local regions, some segmentation lines are obtained. We use a trick called `line segment shortening' to improve the above mentioned methods if overlapped radicals exist in a character. Based on the common writing habits of people, the decision algorithms are proposed to identify the correctness of segmentation lines. Our experimental results are conducted on ten databases of 5401 frequently used Chinese characters that users wrote according to their habits. An average suitable segmentation rate of more than 94% has been obtained, which shows that our algorithm is reliable.