Defect detection system for signature collation based on machine vision

Shuang Liu; Yingjie Tang

doi:10.1117/12.2661032

2 February 2023 Defect detection system for signature collation based on machine vision

Shuang Liu, Yingjie Tang

Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 1246225 (2023) https://doi.org/10.1117/12.2661032
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China

Abstract

Aiming at the declining trend of the overall binding quality of adhesive binding publications, a defect detection system for signature collation based on machine vision is designed. The signature image is collected by the CCD sensor, and the traditional image algorithm is studied by using OpenCV open-source vision software. After preprocessing the signature image to be detected, the Hough transform and affine transformation algorithms are firstly used to extract and describe the features of the target image, calculate the tilt angle of the signature, and correct the tilted image. Then, the template image is matched with the signature image for template matching to locate and extract the region of interest ROI; finally, gray statistical features, perceptual hash and image difference methods are selected to process the template and ROI images, and calculate the matching similarity. The experiment realizes the real-time monitoring and error processing of many kinds of signatures such as word pages, image-text pages and graphic pages, and completes the detection task. The results show that the accuracy of the system defect detection can reach 99%, which has good practicability and stability.

Citation Download Citation

Shuang Liu and Yingjie Tang "Defect detection system for signature collation based on machine vision", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 1246225 (2 February 2023); https://doi.org/10.1117/12.2661032

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