A robust approach for recognition of QR codes with cropped sides

QR code is a commonly used two-dimensional barcode employed in a variety of applications. To simplify the detection and segmentation of QR code, its structure incorporates three finder patterns. Sometimes, code sides are cropped due to printing issues or overlapping. However, most recognition methods refuse to work if at least one of three finder patterns is missing or damaged. This paper proposes an approach that allows reading such codes. It is based on utilizing alignment patterns alongside with finder patterns and the RANSAC scheme to estimate a projective transform that maps the symbol modules to the input image. The effectiveness of the proposed approach is evaluated using the generated dataset called SE-QR-SYN-500. It simulates cropped QR codes, both with and without projective distortions, and is published for the open use. The existing open-source solutions show zero accuracy on this dataset. In comparison, our implementation of the proposed approach demonstrates 77.8% accuracy for straight QR codes and 76.4% accuracy for QR codes with projective distortions.