Using Deep Learning to Improve Detection and Decoding Of Barcodes
Chaoxin Wang, Nicolais L. Guevara, Doina Caragea · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
We propose an end-to-end pipeline for transforming raw images containing barcodes into sharp barcode images that can be accurately decoded. Our pipeline leverages recent deep learning approaches and consists of a rotation-decoupled detector (RDD) for oriented barcode detection and a deblurring model. The deblurring model uses a generative adversarial network (specifically, DeblurGAN-v2) trained on pairs of noisy and sharp images generated using ground truth numeric codes. Evaluation of the proposed pipeline using real barcode images enhanced by the DeblurGAN-v2 model shows a 14% improvement of the decoding rate as compared to the decoding rate obtained on the original images.