Fast and Accurate Recognition for Codes on Complex Backgrounds for Real-Life Industrial Applications

Abstract

In the food and beverage industry, the existing recognition of code characters on the surface of complex packaging usually suffers from low accuracy and low speed. This work presents an efficient and accurate inkjet code recognition system based on the combination of the deep learning and traditional image processing methods. The proposed system mainly consists of three sequential modules, i.e., the characters region extraction by modified YOLOv3-tiny network, the character processing by the traditional image processing methods such as binarization and the modified character projection segmentation, and the character recognition by a CRNN model based on a modified version of MobileNetV3. In this system, only a small amount of tag data has been made and an effective character data generator is designed to randomly generate different experimental data for the CRNN model training. To the best of our knowledge, this report for the first time describes that deep learning has been applied to the recognition of codes on complex background for the real-life industrial application. Experimental results have been provided to verify the accuracy and effectiveness of the proposed model, demonstrating a recognition accuracy of 0.986 and a processing speed of 100 ms per bottle in the end-to-end character recognition system.