Optical Character Detection on Industrial Products

Pasker, Vít; Hlavica, Robert; Grycz, Ondřej; Špačková, Hana; Menšík, Martin

doi:10.37904/metal.2019.772

Cited by 1 publication

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“…The method finds better results under conditions where there is an obvious difference in color contrast and light structure in the image. The threshold value is calculated according to the equation (2).…”

Section: Sauvola Methodsmentioning

confidence: 99%

Automatic selection of binarization method from images with serial numbers on industrial products

Pasker

Grycz

Hlavica

et al. 2020

METAL 2020 Conference Proeedings

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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.

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Section: Sauvola Methodsmentioning

confidence: 99%