Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark

PATTERN RECOGNITION

卷:61

Article

Zhang, Xu-Yao¹  Bengio, Yoshua²  Liu, Cheng-Lin^1,3 

[1] Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China

[2] Univ Montreal, MILA, Montreal, PQ H3C 3J7, Canada

[3] CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China

关键词: Handwriting recognition;    Chinese characters;    Online;    Offline;    Directional feature map;    Convolutional neural network;    Adaptation;   

DOI  :  10.1016/j.patcog.2016.08.005

来源: Elsevier

【 摘 要 】

Recent deep learning based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition (HCCR) by learning discriminative representations directly from raw data. Nevertheless, we believe that the long-and-well investigated domain-specific knowledge should still help to boost the performance of HCCR. By integrating the traditional normalization-cooperated direction-decomposed feature map (directMap) with the deep convolutional neural network (convNet), we are able to obtain new highest accuracies for both online and offline HCCR on the ICDAR-2013 competition database. With this new framework, we can eliminate the needs for data augmentation and model ensemble, which are widely used in other systems to achieve their best results. This makes our framework to be efficient and effective for both training and testing. Furthermore, although directMap+ convNet can achieve the best results and surpass human-level performance, we show that writer adaptation in this case is still effective. A new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer. The adaptation process can be efficiently and effectively implemented in an unsupervised manner. By adding the adaptation layer into the pre-trained convNet, it can adapt to the new handwriting styles of particular writers, and the recognition accuracy can be further improved consistently and significantly. This paper gives an overview and comparison of recent deep learning based approaches for HCCR, and also sets new benchmarks for both online and offline HCCR. (C) 2016 Elsevier Ltd. All rights reserved.

【 授权许可】

Free