Chip Surface Character Recognition Based on OpenCV

Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing. By recognizing the character information on the chip, automated production, quality control, and data collection and analysis can be achieved. This article studies a chip surface character recognition method based on the OpenCV vision library. Firstly, the obtained chip images are preprocessed. Secondly, the template matching method is used to locate the chip position. In addition, the surface characters on the chip are individually segmented, and each character image is extracted separately. Finally, a Support Vector Machine (SVM) is used to classify and recognize characters. The results show that this method can accurately recognize the surface characters of chips and meet the requirements of chip quality inspection.

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Publication Details

Journal
Journal of Electronic Research and Application
Published
2024-08-20
DOI
https://doi.org/10.26689/jera.v8i4.8008
Citations
2
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.70
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article

Chip Surface Character Recognition Based on OpenCV

Lihang Yin
2 citations
Journal of Electronic Research and Application
Industrial Vision Systems and Defect Detection
0.70
article

Chip Surface Character Recognition Based on OpenCV

Lihang Yin
article en
2 citations

Abstract

Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing. By recognizing the character information on the chip, automated production, quality control, and data collection and analysis can be achieved. This article studies a chip surface character recognition method based on the OpenCV vision library. Firstly, the obtained chip images are preprocessed. Secondly, the template matching method is used to locate the chip position. In addition, the surface characters on the chip are individually segmented, and each character image is extracted separately. Finally, a Support Vector Machine (SVM) is used to classify and recognize characters. The results show that this method can accurately recognize the surface characters of chips and meet the requirements of chip quality inspection.

Journal of Electronic Research and ApplicationVol. 8(4)
Open University of China (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 26%
Industrial Vision Systems and Defect Detection
0.70
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