Analysis of Image Preprocessing and Binarization Methods for OCR-Based Detection and Classification of Electronic Integrated Circuit Labeling

Abstract

Automatic recognition and classification of electronic integrated circuits based on optical character recognition combined with the analysis of the shape of their housings are essential to machine vision methods supporting the production of electronic parts, especially small-volume ones in the through-hole technology, characteristic of printed circuit boards. Since such methods utilize binary images, applying appropriate image preprocessing and thresholding methods significantly influences the obtained results, particularly in uncontrolled illumination conditions. Therefore, the examination of various adaptive image binarization algorithms for this purpose is conducted in this paper, together with the experimental verification of the proposed method based on the pixel voting approach.