MICROCHIP MARKING RECOGNITION AND IDENTIFICATION USING A COMPUTER VISION SYSTEM MATHEMATICAL MODEL

The article considers a microcircuit markings recognition and identification using a computer vision system mathematical model that provides automation of electronic component control. An image processing algorithm is proposed, which includes preliminary filtering, binarization and optical character recognition to increase the accuracy of identification. An experimental analysis of the influence of the angle of inclination and the level of illumination on the speed and quality of recognition was carried out, which allowed formulating recommendations on the optimal shooting parameters. The results obtained can be used to create automated quality control systems in electronics production.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-11
DOI
https://doi.org/10.5281/zenodo.15194651
Primary Topic
Image and Object Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MICROCHIP MARKING RECOGNITION AND IDENTIFICATION USING A COMPUTER VISION SYSTEM MATHEMATICAL MODEL

Zenodo (CERN European Organization for Nuclear Research)
Image and Object Detection Techniques
article

MICROCHIP MARKING RECOGNITION AND IDENTIFICATION USING A COMPUTER VISION SYSTEM MATHEMATICAL MODEL

article en

Abstract

The article considers a microcircuit markings recognition and identification using a computer vision system mathematical model that provides automation of electronic component control. An image processing algorithm is proposed, which includes preliminary filtering, binarization and optical character recognition to increase the accuracy of identification. An experimental analysis of the influence of the angle of inclination and the level of illumination on the speed and quality of recognition was carried out, which allowed formulating recommendations on the optimal shooting parameters. The results obtained can be used to create automated quality control systems in electronics production.

Zenodo (CERN European Organization for Nuclear Research)
Amman Arab University (JO), V. N. Karazin Kharkiv National University (UA)
Openalex Percentile: Top 56%
Image and Object Detection Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.