  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.
