An irrelevant variability normalization approach to discriminative training of multi-prototype based classifiers and its applications for online handwritten Chinese character recognition

• Jointly discriminative training of feature transforms and classifier parameters.
• Rprop optimization for SSM-MCE based objective function is adopted.
• Both piecewise linear transform and weighted sum of linear transforms are compared.
• The IVN-based recognizer can be made both compact and efficient by using fast-match.
• Experiments show the effectiveness of our approach over the state-of-the-art.

This paper presents an irrelevant variability normalization (IVN) approach to jointly discriminative training of feature transforms and multi-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. For the IVN approach based on piecewise linear transforms, the corresponding 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. Furthermore, the IVN system using weighted sum of linear transforms outperforms that based on piecewise linear transforms. The effectiveness of the proposed approach is first confirmed using an in-house developed online Chinese handwriting corpus with a vocabulary of 9306 characters, and then further verified on a standard benchmark database for an online handwritten character recognition task with a vocabulary of 3755 characters.