AUTOMATIC SELECTION OF BINARIZATION METHOD FROM IMAGES WITH SERIAL NUMBERS ON INDUSTRIAL PRODUCTS

1 PASKER Vít
Co-authors:
1 GRYCZ Ondřej 1 HLAVICA Robert 1 FORETNÍK Pavel 1 BARČÁKOVÁ Ivana
Institution:
1 VSB - Technical University of Ostrava, Ostrava, Czech Republic, EU, vit.pasker@vsb.cz
Conference:
29th International Conference on Metallurgy and Materials, Brno, Czech Republic, EU, May 20 - 22, 2020
Proceedings:
Proceedings 29th International Conference on Metallurgy and Materials
Pages:
1357-1361
ISBN:
978-80-87294-97-0
ISSN:
2694-9296
Published:
27th July 2020
Proceedings of the conference were published in Web of Science and Scopus.
Metrics:
507 views / 359 downloads
Abstract

The article deals with the automatic selection of the binarization method using advanced methods of artificial intelligence. The input images to the algorithms are images of serial numbers from industrial environments, for example on iron and steel billets, slabs, etc. The surface of these products is in most cases severely damaged by industrial processes, such as traces of cut, rust, noise, surface roughness, etc. Text recognition is a very common topic nowadays. All investigated solutions are based on the fact that each image is binarized by a single defined method and the accuracy of recognition is given only by the quality of learning of the neural network. Especially in an industrial environment, it is difficult to create a universal method for unambiguous methods for text recognition. The innovation described in this article is the automatic selection of the binarization method (from the Bradley, Niblack, Sauvola methods etc.), which increases the accuracy already in the phase before the text recognition itself, which with the subsequent correct combination of filters leads to an overall increase in accuracy.

Keywords: Binarization, image recognition, neural network, image filters

© 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.

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