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

Vít Pasker, Ondřej Grycz, Robert Hlavica, Pavel Foretník, Ivana Barčáková
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引用次数: 0

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.
工业产品序列号图像的二值化自动选择方法
本文讨论了利用先进的人工智能方法自动选择二值化方法。算法的输入图像是来自工业环境的序列号图像,例如钢铁钢坯、板坯等。这些产品的表面在大多数情况下受到工业过程的严重损坏,例如切割痕迹,生锈,噪音,表面粗糙度等。文本识别是当今一个非常普遍的话题。所有研究的解决方案都是基于这样一个事实,即每个图像都是通过单一的定义方法二值化的,识别的准确性仅取决于神经网络的学习质量。特别是在工业环境中,很难为文本识别的无歧义方法创建一个通用的方法。本文所描述的创新是二值化方法的自动选择(来自Bradley、Niblack、Sauvola方法等),它提高了文本识别本身之前阶段的精度,再加上随后正确的滤波器组合,导致整体精度的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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