基于OCR和图像检测的盖章文书图像自动审核方法

doi:10.3969/j.issn.0255-8297.2023.06.012

Abstract

Abstract: In this paper, we design and implement an auto-checking method based on OCR and image detection to replace the time-consuming and error-prone manual work. The method consists of three parts: text recognition, seal recognition, and content checking. For text recognition, we utilize the SegLink algorithm for angled text detection and the CRNN algorithm for variable length end-to-end text recognition. For seal recognition, we employ the YOLOv3 algorithm for seal recognition and extraction, along with the polar coordinate transformation method for seal content recognition. The content checking is based on the preset rules to check the completeness and correctness of the content extracted from the form. Experimental result shows that the proposed method achieves high accuracy in checking stamped document image with seals.

Key words: automated examining, text recognition, seal recognition, convolutional recurrent neural network (CRNN)

CLC Number:

TP391

CAO Jing, CHEN Kang, QI Ning, XIA Pengcheng, QIU Yu. Auto-Checking Stamped Document Image Based on OCR and Image Detection[J]. Journal of Applied Sciences, 2023, 41(6): 1058-1067.

References

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Auto-Checking Stamped Document Image Based on OCR and Image Detection

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