Recognition of expiry data on food packages based on improved DBNet

To prevent products with missing character information from reaching the market, manufacturers need an automatic character recognition method. One of the key problems of this recognition method is to recognise text under complex package patterns. In addition, some products use dot matrix characters to reduce printing costs, which makes text extraction more difficult. We propose a character detection algorithm using DBNet as the base network, combined with the Convolutional Block Attention Module (CBAM) to improve its feature extraction of characters in complex contexts. After the character area has been located by the detection algorithm, it is intercepted and fed into a fully convolutional character recognition network to achieve print character recognition. We use ResNet as the backbone network and CTC loss for training. In addition, the CBAM module was added to the backbone network to enhance its recognition of dot matrix characters. The algorithm was finally deployed on the jetson nano. The experimental results show that the character detection accuracy reaches 97.9%, an improvement of 1.9% compared to the original network. As for the character recognition algorithm, the inference speed is doubled when deployed to the nano platform compared to the CRNN network, with an accuracy of 97.8%.