Unsupervised Adaptation of Neural Networks for Chinese Handwriting Recognition

Abstract

Writer adaptation is an important topic in handwriting recognition, which can further improve the performance of writer-independent recognizer. In this paper, we propose combining the neural network classifier with style transfer mapping (STM) for unsupervised writer adaptation, which only require writer-specific unlabeled data, and therefore is more common and efficient compared to supervised adaptation. We use some techniques like dropout, ReLU, momentum, and deeply supervised strategy to improve the performance of the neural network classifier. For a specific writer in the test data, an adaptation layer is added to the pre-trained neural network classifier. In adaptation process, only the parameters in adaptation layer are updated while other parameters of the neural network are kept unchanged. To train the adaptation layer, we use the same technology as STM learning but redefine the source point set, target point set and the corresponding confidence. Experiments on the online Chinese handwriting database CASIA-OLHWDB1.1 demonstrate that our method is very efficient and effective in improving classification accuracy. The experimental results also show that our proposed method outperforms the previous proposed learning vector quantization (LVQ) and modified quadratic discriminant function (MQDF) with STM methods for writer adaptation.