Affine Distortion Compensation for an Isolated Online Handwritten Chinese Character Using Combined Orientation Estimation and HMM-Based Minimax Classification

This paper presents a new approach to compensating affine distortion of an isolated online handwritten Chinese character. The input sample is first analyzed by using a character-structure-guided orientation estimation approach. If necessary, the orientation hypotheses are refined based on confidence evaluation of two pre-classifiers. Depending on the number of possible orientations, an HMM-based minimax classification approach is then used to estimate an affine transformation against either the original sample or the compensated sample with the previously identified orientation. The final compensated sample can be derived accordingly using the estimated affine transformation. The effectiveness of the proposed approach is demonstrated by recognition experiments using distorted samples generated artificially from the popular Nakayosi and Kuchibue Japanese character databases.