Error Reduction by Confusing Characters Discrimination for Online Handwritten Japanese Character Recognition

To reduce the classification errors of online handwritten Japanese character recognition, we propose a method for confusing characters discrimination with little additional costs. After building confusing sets by cross validation using a baseline quadratic classifier, a logistic regression (LR) classifier is trained to discriminate the characters in each set. The LR classifier uses subspace features selected from existing vectors of the baseline classifier, thus has no extra parameters except the weights, which consumes a small storage space compared to the baseline classifier. In experiments on the TUAT HANDS databases with the modified quadratic discriminant function (MQDF) as baseline classifier, the proposed method has largely reduced the confusion caused by non-Kanji characters.