Combining Deep Learning and Computer Vision for Barcode Recognition

Last week, I trained a YOLOv3 model and a YOLOv3-tiny model to do barcode localization via deep learning. By comparing their performance, I dropped YOLOv3, because YOLOv3-tiny is much faster. I am satisfied with the QR code detection speed by running the YOLOv3-tiny model on my GeForce RTX2060 graphics card. In this article, I will power Darknet to decode QR code by integrating Dynamsoft C/C++ barcode SDK. My goal is to explore whether it is possible to utilize deep learning to boost barcode recognition performance.

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Darknet with CUDA: Train YOLO Model for QR Code Detection on Windows

In my previous article, I shared how to integrate Dynamsoft Barcode Reader to LabelImg for annotating barcode objects. It is time to take a further step to make some custom models for barcodes. In this article, I will go through the process that I used Darknet to train YOLO v3 models for QR code detection.

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How to Annotate Barcode Object with LabelImg for Machine Learning

LabelImg is a free and open-source image annotation tool written in Python and Qt5. It supports Pascal VOC format and Yolo format that are commonly adopted by mainstream machine learning frameworks. If you are interested in barcode object detection, you can use the tool to annotate different barcode symbologies with bounding boxes. In this article, I will share how to quickly download a bunch of barcode images from Google and utilize Dynamsoft Barcode Reader SDK to automatically add label names.

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