Batch Reading Densely Arranged QR Codes

Abstract

This paper presents BatchQR, a mobile APP that can batch read the densely arranged QR codes attached to caps of the tubes and vials in clinical and biological labs. The basic idea of BatchQR is to detect each code in the image and then decode in an one-by-one manner. However, the unique characteristics of the QR code and the application scenario bring technical challenges: First, off-the-shelf lightweight object detection mechanisms are unable to distinguish those densely arranged codes that are highly similar to each other; second, the focus area of the camera is limited, which blurs or distorts parts of the image. To this end, we propose a lightweight code detection mechanism, which can adaptively adjust operating parameters to identify densely arranged QR codes in practice. We also propose a simple but effective image refocus mechanism, which takes an auto-focused image and multiple refocused ones, and then replaces the blurred or distorted code parts with the high-quality counterparts in the refocused images. Comprehensive experimental results show that BatchQR can read 160-180 Version 1-L QR codes in batch with 90%-95% accuracy in 10-14s, which is only 4% of the time consumed by the regular QR code reader in the same situation.