Abstract
In the paper, an application of the recently proposed approach to hybrid image binarization based on pixel voting is considered for industrial images. Since such images typically contain the text embossed or engraved in metal nameplates, often non-uniformly illuminated, a proper binarization of such images is usually much harder than for scanned document images, or even for the photos of text documents. Assuming that no single method would be the best solution for such images, a hybrid solution, based on the combination of multiple algorithms using pixel voting, has been recently proposed for document images. The obtained experimental results for the dataset of “industrial” images confirm the usefulness of this approach and the proposed combinations of previously developed algorithms outperform the other methods, making it possible to increase the OCR accuracy also for demanding images containing light reflections and shadows.
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Michalak, H., Okarma, K. (2023). Adaptive Binarization of Metal Nameplate Images Using the Pixel Voting Approach. In: Chmielewski, L.J., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2022. Lecture Notes in Networks and Systems, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-031-22025-8_10
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