Universal Barcode Detector via Semantic Segmentation
This work addresses the need for efficient and universal barcode detection in commercial applications, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of detecting various barcode types in diverse environments by introducing a fast and robust deep learning detector based on semantic segmentation, achieving a state-of-the-art detection rate of 0.995 on the ArTe-Lab 1D Medium Barcode Dataset and real-time performance on CPU.
Barcodes are used in many commercial applications, thus fast and robust reading is important. There are many different types of barcodes, some of them look similar while others are completely different. In this paper we introduce new fast and robust deep learning detector based on semantic segmentation approach. It is capable of detecting barcodes of any type simultaneously both in the document scans and in the wild by means of a single model. The detector achieves state-of-the-art results on the ArTe-Lab 1D Medium Barcode Dataset with detection rate 0.995. Moreover, developed detector can deal with more complicated object shapes like very long but narrow or very small barcodes. The proposed approach can also identify types of detected barcodes and performs at real-time speed on CPU environment being much faster than previous state-of-the-art approaches.