Handwritten Text Recognition of Imagery Acquisition Sheet in Tobacco Industry Using an Improved CRNN Network
Authors: 
Wei Liu, Yan Wang, Lang-chao Qiao, Jin-lu Wang, Yi-ran Wei, Hai Liu, Xin-gang Yang, Wen-tao FengAuthors Info & Claims
Wei Liu, Yan Wang, Lang-chao Qiao, Jin-lu Wang, Yi-ran Wei, Hai Liu, Xin-gang Yang, Wen-tao FengAuthors Info & ClaimsPages 649 - 656
Abstract
In the tobacco industry, the acquisition sheet is a kind of document used by the tobacco monopoly administration to record the information about the acquisition of fake and illicit cigarettes when the tobacco crime is seized by the "Internet Plus Express". To automatically identify the information helps to realize the automation of information management of tobacco monopoly administration department, and then build delivering for smoke crime management and prediction system, thus to deliver smoke involved crime effectively, and realize the wisdom of the tobacco industry monopoly, so to handwriting recognition research of the acquisition sheet image is very necessary. In this paper, an improved CRNN network text recognition method is proposed. Firstly, the acquisition sheet image with interference such as shadow and seal is preprocessed based on color channel separation method. Then, in the presence of printed text, the YoloX model is used to detect the handwritten text in the image, and the handwritten text line dataset is obtained. Then, the CNN network is improved by adding Swish activation function and attention mechanism to obtain the depth image features of handwritten text line data. BiLSTM network predicts the input feature sequence and finally realizes the recognition of handwritten text. Through the algorithm in this paper, the detection of handwritten text in the acquisition sheet image reaches an average accuracy of 0.983. The experimental results show that, compared with the CRNN model, the improved CRNN model in this paper has a better effect on the recognition of handwritten text in the acquisition sheet image.
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- Handwritten Text Recognition of Imagery Acquisition Sheet in Tobacco Industry Using an Improved CRNN Network
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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