BAN, A Barcode Accurate Detection Network

Yuan Tian, Zhaohui Che, Guangtao Zhai, Zhiyong Gao

Abstract: Barcode detection has been elaborately investigated before. Traditional hand-crafted feature based methods achieve promising performances across some simple and high-quality test databases. However, they usually fall short as a part of barcode decoding pipeline when dealing with challenging scenarios, because of the low robustness and accuracy achieved by low-level features. In this paper, we present a barcode detection network pipeline, designed to generate accurate barcode regions with category labels. We also build a rich barcodes detection dataset-Mixed Barcode Dataset. Experimental results conducted on different barcode databases show the superiority of our method in terms of F-score and decoding accuracy compared to the state of arts.