Handwritten Character Recognition Using HMM Based on Optimal Discriminant Transformation
基于最佳鉴别变换的HMM手写数字字符识别

Handwritten character recognition using the hidden Markov model (HMM) has been an active research topic for the past decade. One of the major problems, however, is that the handwritten characters may not exhibit consistent patterns due to different people's different writing styles. To enhance HMM's encoding stability and to reduce its modeling complexity, we propose a new approach in this paper. Specifically, we first obtain a set of uncorrelated optimal discriminant vectors by conducting feature extraction and dimension reduction using the uncorrelated Foley-Sammon transformation. Next, using a new feature space spanned by the optimal discriminant vectors, we obtain the projection coefficients of the raw data onto this new feature space. We then use these coefficients to form the observation sequence of the HMM. Because the uncorrelated Foley-Sammon transformation ensures minimum intra-class distance and maximum inter-class distance, it significantly improves HMM's encoding stability and difference classes' separability. In fact, the transformation allows different characters to be separable in many projection directions. To validate the accuracy and robustness of the proposed approach, we conduct experiments on the widely used US Postal Service (USPS) data set. Experiments show that the integration of the uncorrelated Foley-Sammon transformation and the HMM performs very well, achieving a recognition rate of 92%. It not only is better than regular HMM, but also is superior to the widely used nerual network based approaches.