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Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments
Li, H.; Ma, Y.; Bao, H.; Zhang, Y. Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments. Appl. Sci.2023, 13, 4533.
Li, H.; Ma, Y.; Bao, H.; Zhang, Y. Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments. Appl. Sci. 2023, 13, 4533.
Li, H.; Ma, Y.; Bao, H.; Zhang, Y. Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments. Appl. Sci.2023, 13, 4533.
Li, H.; Ma, Y.; Bao, H.; Zhang, Y. Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments. Appl. Sci. 2023, 13, 4533.
Abstract
Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a probabilistic Hough transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive probability Hough transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
