On method of training dictionaries for handwritten character recognition using relaxation matching

A training method of dictionary characters is presented for handwritten character recognition by the model based on relaxation approach. The dictionary characters are constructed by analyzing the structure of several training characters using curved line approximation and relaxation matching. The performance of the constructed dictionary characters was verified by the experiment. 956 (chars.) × 20 (sets) in the standard database ETL-8 (Japanese KANJI and HIRAGANA) are used as sample characters for training and 10 (sets) are used as test characters (5 known sets and 5 unknown sets). The mean of the number of constructed dictionary characters is 3.81 for each category, and recognition rate is 99.96% on the known sets and 99.73% on the unknown sets