An Irrelevant Variability Normalization Based Discriminative Training Approach for Online Handwritten Chinese Character Recognition

This paper presents a discriminative training approach to irrelevant variability normalization (IVN) based joint training of feature transforms and prototype-based classifier for recognition of online handwritten Chinese characters. A sample separation margin based minimum classification error criterion is adopted in IVN-based training, while an Rprop algorithm is used for optimizing the objective function. The IVN-trained recognizer can be made both compact and efficient by using a two-level fast-match tree whose internal nodes coincide with the labels of feature transforms. The effectiveness of the proposed approach is confirmed on an online handwritten character recognition task with a vocabulary of 9,306 characters.