摘要
要提高脱机手写字符识别的识别率,关键是特征的提取。主曲线是主成分分析的非线性推广,是通过数据分布“中间”并满足“自相合”的光滑曲线。通过对现有主曲线算法分析可知:软 K 段主曲线算法对提取出分布在弯曲度很大或相交曲线周围的数据的主曲线效果较好。因此本文尝试用该主曲线算法来提取脱机手写字符的结构特征。实验结果表明,利用该主曲线算法来提取脱机手写字符的结构特征不但是可行的,而且取得较好的实验效果。它为脱机手写字符特征提取的研究提供了一条新途径。
Extraction of features is critical to improve the recognition rate of off-line handwritten characters. Principal curves are nonlinear generalizations of principal components analysis. They are smooth self-consistent curves that pass through the ＂middle＂ of the distrihutlon. By analysis of existed principal curves, we learn that a soft k-segments algorithm for principal curves exhihits good performance in such situations in which the data sets are concentrated around a highly curved or self-intersecting curves. Therefore, we attempt to use the algorithm to extract structural features of off-line handwritten characters. Experiment results show that the algorithm is not only feasible for extraction of structural features of characters, but also exhibits good performance. The proposed method can provide a new approach to the research for extraction of structural features of characters.
出处
《计算机科学》
CSCD
北大核心
2006年第1期229-231,共3页
Computer Science
基金
国家自然科学基金项目(60175016
60475019)。
