QR Code Detection with Perspective Correction and Decoding in Real-World Conditions Using Deep Learning and Enhanced Image Processing

David Joshua Corpuz, Lance Victor Del Rosario, Jonathan Paul Cempron, Paulo Luis Lozano, Joel Ilao

2025

Abstract

QR codes have become a vital tool across various industries, facilitating data storage and accessibility in compact, scannable formats. However, real-world environmental challenges, including lighting variability, perspective distortions, and physical obstructions, often impair traditional QR code readers such as the one included in OpenCV and ZBar, which require precise alignment and full code visibility. This study presents an adaptable QR code detection and decoding system, leveraging the YOLO deep learning model combined with advanced image processing techniques, to overcome these limitations. By incorporating edge detection, perspective transformation, and adaptive decoding, the proposed method achieves robust QR code detection and decoding across a range of challenging scenarios, including tilted angles, partial obstructions, and low lighting. Evaluation results demonstrate significant improvements over traditional readers, with enhanced accuracy and reliability in identifying and decoding QR codes under complex conditions. These findings support the system’s application potential in sectors with high demands for dependable QR code decoding, such as logistics and automated inventory tracking. Future work will focus on optimizing processing speed, extending multi-code detection capabilities, and refining the method’s performance across diverse environmental contexts.

Paper Citation

in Harvard Style

Corpuz D., Rosario L., Cempron J., Lozano P. and Ilao J. (2025). QR Code Detection with Perspective Correction and Decoding in Real-World Conditions Using Deep Learning and Enhanced Image Processing. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 685-690. DOI: 10.5220/0013287200003912

in Bibtex Style

@conference{visapp25,
author={David Corpuz and Lance Rosario and Jonathan Cempron and Paulo Lozano and Joel Ilao},
title={QR Code Detection with Perspective Correction and Decoding in Real-World Conditions Using Deep Learning and Enhanced Image Processing},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={685-690},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013287200003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - QR Code Detection with Perspective Correction and Decoding in Real-World Conditions Using Deep Learning and Enhanced Image Processing
SN - 978-989-758-728-3
AU - Corpuz D.
AU - Rosario L.
AU - Cempron J.
AU - Lozano P.
AU - Ilao J.
PY - 2025
SP - 685
EP - 690
DO - 10.5220/0013287200003912
PB - SciTePress