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.

钞票序列号识别（SNR）在银行业务中扮演着重要角色，并且最近引起了很多关注。然而，大多数现有的SNR方法将字符分割和字符分类作为两个单独的步骤，因此SNR的准确性很大程度上取决于字符分割，由于背景复杂和照明不均匀，这是一个具有挑战性的问题。本文将SNR引入序列预测问题中，将这两个步骤整合到一个统一的网络中，我们提出了一种基于深度学习的序列号识别网络，该网络可以端到端地进行训练以避免初步字符分割，分为以下三个步骤。首先，采用改进的卷积神经网络提取输入图像的特征序列。第二，特征序列被用作双向递归神经网络（BRNN）的输入，在这种情况下不需要字符分割。最后，使用连接器时间分类对BRNN的输出进行解码，从而实现标签识别。实验结果表明，该方法在准确性和效率上均优于最新方法：实现了人民币（RMB）字符和序列号识别，准确度分别为99.96％和99.56％。