[1]GUO Xiaofeng,WANG Yaonan,MAO Jianxu.IC chip character segmentation and recognition method based on geometric features[J].CAAI Transactions on Intelligent Systems,2020,15(1):144-151.[doi:10.11992/tis.201904028]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 1
Page number:
144-151
Column:
人工智能院长论坛
Public date:
2020-01-05
- Title:
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IC chip character segmentation and recognition method based on geometric features
- Author(s):
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GUO Xiaofeng1; 2; WANG Yaonan1; 2; MAO Jianxu1; 2
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1. College of Electrical and Information Engineering, Hu’nan University, Changsha 410082, China;
2. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hu’nan University, Changsha 410082, China
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- Keywords:
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IC chip; character segmentation; character recognition; aspect ratio; area ratio; geometric characteristics; minimum circumscribed circle; pixel difference
- CLC:
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TP391
- DOI:
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10.11992/tis.201904028
- Abstract:
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To solve the problem of character segmentation and recognition in IC chip, a method based on character geometric features and a normalization and relocation method based on the smallest circumferential circle of characters are proposed. The recognition is accomplished by template matching based on pixel difference. Firstly, the histogram equalization is applied to the chip image, and the auxiliary circle is used to locate the center line and correct the image. The ROI region is located and processed by mean of binarization. Subsequently, the binary ROI region image is segmented into characters, and the geometric features of the characters are used as the judgment conditions, thus the correct segmentation of defective characters is completed. Then, the maximum contour is extracted from the single character image, and the minimum circumscribed circle of the contour is used to normalize and relocate the characters. Finally, the normalized characters are differentially recognized. Four kinds of chip samples are collected for experiments. The results show that the method can achieve accurate segmentation of chip characters, and the accuracy of defective characters is 90%. The average recognition time of single character is 4.6 ms, and the recognition accuracy is 99.4%.