Similar Handwritten Chinese Character Recognition Using Discriminative Locality Alignment Manifold Learning
The discriminant analysis for Similar Handwritten Chinese Character Recognition (SHCR) is essential for the improvement of handwritten Chinese character recognition performance. In this paper, a new manifold based subspace learning algorithm, Discriminative Locality Alignment (DLA), is introduced into SHCR. Experimental results demonstrate that DLA is consistently superior to LDA (Linear Discriminant Analysis) in terms of discriminate information extraction, dimension reduction and recognition accuracy. In addition, DLA reveals some attractive intrinsic properties for numeric calculation, e.g. it can overcome the matrix singular problem and small sample size problem in SHCR.