Writer Adaptation Using Bottleneck Features and Discriminative Linear Regression for Online Handwritten Chinese Character Recognition

This paper presents a novel approach to writer adaptation using bottleneck features and discriminative linear regression for the recognition of online handwritten Chinese characters. First, bottleneck features extracted from a bottleneck layer of a deep neural network representing a nonlinear and discriminative transformation of the input features are verified to be much more effective in adaptation of writing styles than the conventional features after linear discriminant analysis transformation. Second, discriminative linear regression via a so-called sample separation margin based minimum classification error criterion is adopted for writer adaptation. The experiments on an in-house developed online Chinese handwriting corpus with a vocabulary of 15,167 characters and testing data collected from user inputs of Smartphones show that our proposed approach can achieve very significant improvements of recognition accuracy compared with a state-of-the-art adaptation approach for writer adaptation.