On-line cursive Kanji character recognition using stroke-based affine transformation

This paper describes a distortion-tolerant online Kanji character recognition method using stroke-based affine transformation (SAT). The first part of the method determines one-to-one stroke correspondence between an input pattern and each reference pattern. The second part applies optimal SAT to each stroke of the input pattern to absorb handwriting distortion. The last part calculates the inter-pattern distance between the reference pattern and the SAT-superimposed input pattern. Only a single reference pattern for each of 2,980 Kanji character categories is generated by using training data written carefully with the correct stroke-number and stroke-order. Recognition tests are made using two kinds of test data in the square style and in the cursive style written by 36 different people; recognition rates of 98.4% and 96.0% are obtained.