SNRNet: A Deep Learning-Based Network for Banknote Serial Number Recognition

The banknote serial number recognition (SNR) plays an important role in the banking business and attracts much attention recently. However, most of the existing SNR methods take character segmentation and character classification as two separate steps, so that the accuracy of SNR heavily relies on the character segmentation, which is a challenging problem due to complicated background and uneven illumination. In this paper, the SNR is cast into a sequence prediction problem, which integrates such two steps into a unified network, and we propose a deep learning-based serial number recognition network, which can be trained end-to-end to avoid the preliminary character-segmentation with three steps as follow. First, the improved convolutional neural networks are employed to extract the feature sequence of the input image. Second, the feature sequence is used as an input to the bidirectional recurrent neural networks (BRNNs), where the character segmentation is not required. Finally, the label recognition is implemented using the connectionist temporal classification to decode the BRNNs’ output. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in both accuracy and efficiency: it achieves character and serial number recognition of the renminbi (RMB) with accuracies 99.96% and 99.56%, respectively.