A new fully convolutional neural network architecture with text kernel for offline handwritten chinese text recognition

Yuanyuan Li; Xinguang Yang; Zhiwei Ren

doi:10.1117/12.3032806

5 July 2024 A new fully convolutional neural network architecture with text kernel for offline handwritten chinese text recognition

Yuanyuan Li, Xinguang Yang, Zhiwei Ren

Author Affiliations +

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841J (2024) https://doi.org/10.1117/12.3032806
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

It’s challenging to optimize offline handwritten Chinese recognition as a topic of pattern recognition as the consequence of the complex structures of Chinese characters. To solve this problem, many data pre-processing methods and neural network architectures have been optimized to improve the recognition accuracy. To this end, we propose a fully convolutional neural network architecture with text kernel for offline handwritten Chinese text recognition in this paper. To make the features of the image clearer, the text kernel which can align the characters of handwritten Chinese text is implemented. This novel fully convolutional neural network captures the feature maps in two branch and promote the efficiency of neural networks. Experimenting on the CASIA-HWDB 2.0-2.2 datasets, our proposed method achieves a competitive performance and the accuracy achieves 97.87% with language model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yuanyuan Li, Xinguang Yang, and Zhiwei Ren "A new fully convolutional neural network architecture with text kernel for offline handwritten chinese text recognition", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841J (5 July 2024); https://doi.org/10.1117/12.3032806

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KEYWORDS

Convolutional neural networks

Image segmentation

Convolution

Education and training

Image processing

Tunable filters

Feature extraction

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