Online Handwriting Tibetan Character Recognition Based on Two-Dimensional Discriminant Locality Alignment.

Discriminant Locality Alignment (DLA) has been successfully applied in handwriting character recognition. In this paper, a new manifold based subspace learning algorithm, which is called Two-dimensional Discriminant Locality Alignment (2DDLA) algorithm, is proposed for online handwriting Tibetan character recognition (OHTCR). The proposed algorithm integrates the idea of DLA and two-dimensional feature extraction algorithm. At first, extracting direction feature matrix and edge feature matrix of Tibetan character respectively, they are together formed original feature matrix. Then, in part optimization stage, for each character sample, a local patch is built by the given sample and its neighbors, and an object function is designed to preserve local discriminant information. Third, in whole alignment stage, the alignment trick is used to align all part optimizations to the whole optimization. The projection matrix can be obtained by solving a standard eigen-decomposition problem. Finally, a SMQDF classifier is used training and recognition. Experimental results demonstrate that 2DLDA is superior to LDA and IMLDA in terms of recognition accuracy. In addition, 2DLDA can overcome the matrix singular problem and small sample size problem in OHTCR.