# Universal Barcode Detector via Semantic Segmentation

## TL;DR

This paper presents a fast, robust deep learning-based barcode detector using semantic segmentation that can identify various barcode types in different environments, achieving state-of-the-art accuracy and real-time performance on CPU.

## Contribution

A novel semantic segmentation-based deep learning model capable of detecting multiple barcode types simultaneously with high accuracy and speed, including complex shapes.

## Key findings

- Detection rate of 0.995 on ArTe-Lab dataset
- Handles complex barcode shapes like long, narrow, or small barcodes
- Operates in real-time on CPU, outperforming previous methods

## Abstract

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.