Open Access

ARTICLE

End-to-end Handwritten Chinese Paragraph Text Recognition Using Residual Attention Networks

Yintong Wang^1,2,*, Yingjie Yang², Haiyan Chen³, Hao Zheng¹, Heyou Chang¹

1 School of Information Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China
2 Institute of Artificial Intelligence, De Montfort University, Leicester, LE1 9BH, United Kingdom
3 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

* Corresponding Author: Yintong Wang. Email:

Intelligent Automation & Soft Computing 2022, 34(1), 371-388. https://doi.org/10.32604/iasc.2022.027146

Received 11 January 2022; Accepted 24 February 2022; Issue published 15 April 2022

Abstract

Handwritten Chinese recognition which involves variant writing style, thousands of character categories and monotonous data mark process is a long-term focus in the field of pattern recognition research. The existing methods are facing huge challenges including the complex structure of character/line-touching, the discriminate ability of similar characters and the labeling of training datasets. To deal with these challenges, an end-to-end residual attention handwritten Chinese paragraph text recognition method is proposed, which uses fully convolutional neural networks as the main structure of feature extraction and employs connectionist temporal classification as a loss function. The novel residual attention gate block is more helpful in extracting essential features and making the training of deep convolutional neural networks more effective. In addition, we introduce the operations of batch bilinear interpolation which implement the mapping of two dimension text representation to one dimension text line representation without any position information of characters or text lines, and greatly reduce the labeling workload in preparing training datasets. In experimental, the proposed method is verified with two widely adopted handwritten Chinese text datasets, and achieves competitive results to the current state-of-the-art methods. Without using any position information of characters and text line, an accuracy rate of 90.53% is obtained in CASIA-HWDB test set.

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Keywords

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