RugbyNet: an efficient CNN architecture for offline unconstrained handwritten Chinese character recognition

Li Xudong; Zhao Caiyun; Zhang Haiyang

doi:10.1117/12.2684946

19 October 2023 RugbyNet: an efficient CNN architecture for offline unconstrained handwritten Chinese character recognition

Li Xudong, Zhao Caiyun, Zhang Haiyang

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Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090Z (2023) https://doi.org/10.1117/12.2684946
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Recently, neural networks have been used for handwritten Chinese character recognition (HCCR). While we have seen very high recognition accuracy for constrained Chinese handwriting, it remains a challenging task to recognize unconstrained Chinese handwriting. This paper proposes a new network architecture based on convolutional neural networks (CNNs), called RugbyNet. A special characteristic of the network is the rugby-like structure which is capable of reducing the number of multiply-accumulate operations and the required storage. And the convolutional blocks in the rugby-like structure are guided and designed by the speed, a direct indicator of computational complexity, to keep the model efficient while reducing the cost of memory access. Besides, We further proposed three data augmentation methods, extrusion, expansion, and distortion, to generate synthetic samples from the fixed sample set. These synthetic samples can then be used for training. Experimental results show that the recognition accuracy of the proposed RugbyNet on the ICDAR-2013 offline HCCR competition dataset reaches 97.77%, and enables a relative 4.70% error reduction compared to the current single-network method MCANet trained only on handwritten dataset.

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Li Xudong, Zhao Caiyun, and Zhang Haiyang "RugbyNet: an efficient CNN architecture for offline unconstrained handwritten Chinese character recognition", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090Z (19 October 2023); https://doi.org/10.1117/12.2684946

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KEYWORDS

Data modeling

Education and training

Optical character recognition

Network architectures

Convolutional neural networks

Extrusion

Distortion

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