Lightweight neural network-based pipeline for barcode image preprocessing

Barcode scanning greatly benefited from deep learning research, as well as the image processing stages included in its workflow. These stages commonly handle pre-processing tasks like localizing barcode symbols in the input image, identifying their type, and normalizing the found regions. They are especially important when there is no a priori knowledge of input image capturing conditions. Thus, a case of multiple barcode recognition within a unique image drastically differs from a single barcode processing in video stream via smartphone. We assess how accuracy of these stages affects the accuracy of the whole barcode scanning as its best and propose a lightweight neural network-based pipeline implementing tasks listed above. To perform this assessment and evaluate the performance of the proposed pipeline elements, we conduct a series of experiments using the set of popular open source scanners, including OpenCV, WeChat, ZBar, ZXing and ZXing-cpp over the SE-barcode and Dubska datasets. These experiments reveal how the proposed pipeline can be configured for optimum speed and accuracy performance depending on the objective and the chosen scanner.