Barcode reading has various applications in warehouses and as such is critical to supply chain automation. In this project, we develop a pipeline for decoding 1D and 2D segmented barcode images. Our pipeline consists of two parts: denoising network and angle correction. The denoising network deals with removing noise so as to improve the decoding rate of barcodes using open source software such as Zerbra. Angle correction has to do with applying rotation algorithms to segmented barcode images without sacrificing their spatial resolution and introducing anti-aliasing.
Shinwoo Choi, UC Berkeley
Anup Hiremath, UC Berkeley
Avideh Zakhor, UC Berkeley,Link
Cen Zhihao, Amazon.com, Inc.
Barcodes are digital signs made of adjacent and alternating black and white smaller rectangles. Despite the great progress made in deep learning, decoding them in high-resolution images has proven to be a difficult task. Over the years, barcodes have increasingly become part of human interaction in many fields. In administration, they are used to encode, save, and retrieve various users’ information; in businesses such as grocery stores, they are used to track sales and inventories and in hospitals, they are used to track and retrieve patients' data. More interestingly, in warehouses, their detection will facilitate the automation process involved in manipulating different packages. In this project, we will deal with denoising, rectifying and decoding of barcodes that have already been segmented from an image. By rectifying we mean detecting the angle associated with rotated barcodes. We are investigating denoising deep neural networks for noise removal, and Hough transform for determining the rotation angle of the barcode images. We plan to test our approach on a database of synthetically generated images.