Seal Text Detection and Recognition Algorithm with Angle Optimization Network

Jinsheng XIAO; Tao ZHAO; Wenxin XIONG; Tian YANG; Weiqing YAO

doi:10.11999/JEIT201008

Volume 43 Issue 11

Nov. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(11): 3327-3334

Jinsheng XIAO, Tao ZHAO, Wenxin XIONG, Tian YANG, Weiqing YAO. Seal Text Detection and Recognition Algorithm with Angle Optimization Network[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3327-3334. doi: 10.11999/JEIT201008

Citation:

Jinsheng XIAO, Tao ZHAO, Wenxin XIONG, Tian YANG, Weiqing YAO. Seal Text Detection and Recognition Algorithm with Angle Optimization Network[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3327-3334. doi: 10.11999/JEIT201008

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Seal Text Detection and Recognition Algorithm with Angle Optimization Network

doi: 10.11999/JEIT201008

1.
School of Electronic Information, Wuhan University, Wuhan 430072, China
2.
State Grid Hubei Information & Telecommunication Company Limited, Wuhan 430077, China

Funds: The National Natural Science Foundation of China (61471272), The Technology Project of State Grid Hubei Electric Power Co., Ltd. (52153318004G)

Received Date: 2020-11-30
Rev Recd Date: 2021-03-26

Available Online: 2021-04-15

Publish Date: 2021-11-23

Abstract

Abstract

Using the methods of Optical Character Recognition (OCR) to detect and recognize the seal characters can speed up the classification speed and identification efficiency of all kinds of contracts. According to the characteristics of the cycle seal characters arranged in a ring, polar coordinate conversion is used to preprocess the seal characters, which overcomes the problem that the direction of the seal characters is not uniform. The Connectionist Text Proposal Network (CTPN) with angle information is used to detect the undulating text area, and the Bezier curve is used to achieve the accurate detection of the seal area. Finally, a method combined with the attention mechanism and the matching algorithm is used to recognize the detected text area and the seal text content is obtained. Using this algorithm to test the self-made Chinese seal data set, the F-measure of the seal content can reach 84.73%, and the recall rate of the character recognition is 84.4%, which shows that this algorithm can detect and recognize the seal content effectively, and has an important meaning for the research of document classification and identification.
- Image processing,
- Seal recognition,
- Recurrent neural networks,
- Polar coordinate conversion

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References(15)

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