Abstract
Deep learning based methods have been extensively used in Handwritten Chinese Character Recognition (HCCR) and significantly improved the recognition accuracy in recent years. Famous networks like GoogLeNet and deep residual network have been applied to this field and improved the recognition accuracy. While the structure of the neural network is crucial, the training methodology also plays an important role in deep learning based methods. In this paper, a new data generation method is proposed to increase the size of the training database. Chinese characters could be classified into different kinds of structures according to the radical components. Based on this, the proposed method segments the character images into sub-images and recombines them into new character samples. The generated database, including recombined characters and rotated characters, could improve the performance of current CNN models. We also apply the recently proposed and popular center loss function to further improve the recognition accuracy. Tested on ICDAR 2013 competition database, the proposed methods could achieve new state-of-the-art with a 97.53% recognition accuracy.
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Luo, W., Zhai, G. (2018). Offline Handwritten Chinese Character Recognition Based on New Training Methodology. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_22
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DOI: https://doi.org/10.1007/978-981-10-8108-8_22
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