An Edge-driven Framework for Barcode Localization and Recognition Using OpenCV

Barcodes have become a part of our lives, from scanning groceries at the supermarket to buying apparel at the mall. Barcodes are used extensively by all businesses, whether retail or production lines at a factory. Thus, it becomes necessary to scan all the barcodes efficiently with the barcode scanner. Scanning these barcodes seems like a cakewalk, but there is a lot of work behind the scenes. Barcodes may not be scanned properly if they are blurred, not well-lit or if the image of the barcode is rotated. This study has taken into account such barcodes with some environmental noise and applied traditional edge-based processing with Pyzbar. The traditional edge detection techniques that were applied on the barcode images are Canny, Sobel, Scharr and Prewitt. These techniques were applied to the Medium Barcode 1D dataset. The dataset is an open dataset. On applying the edge-based techniques, it was found that Scharr obtained the highest F1-score of 0.697 with a success rate of 76.15%. Though various machine learning and deep learning methods are present, they are computationally expensive. Thus, traditional edge-based processing must be evaluated as they consume less computing resources and memory.