关键词: 样本相似性, HMMs, 单分类器, 后验概率调整

Abstract: Sample-based similarity is an important decision factor for classifying. But in conventional HMMs framework, posterior probability is the only base for classifying. This paper presents a new approach to recognize off-line handwritten character by combining sample similarity and HMMs. In order to measure the similarity between samples, three types of distance are selected and tested. Then the normalized distance measure is employed to adjust the posterior probabilities of HMMs’ outputs. Experimental results confirm the performance of the proposed methods. The recognition rate of the new approach is higher than traditional HMMs, but the recognition speed declines little.

Key words: : Sample similarity, HMMs, single classifier, the adjustment of posterior probability