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

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Abstract

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.

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Acknowledgements

The work was supported in part by National Natural Science Foundation of China under Grants 61773127, 61705047 and 61727810, Ten Thousand Talent Program approved in 2018, Guangdong Province Foundation Grant 2019B1515120036, Natural Science Foundation of Guangdong Province under Grant 2018A030313306, Guangzhou Science and Technology Foundation under Grant 201802010037, and Key Areas of Research and Development Plan Project of Guangdong under Grant 2019B010147001.

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Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Zhijie Lin, Zhaoshui He, Peitao Wang, Beihai Tan, Jun Lu & Yulei Bai

  2. Guangdong Engineering Center of Auto-Detection Technology for Intelligent Manufacturing, Guangzhou, 510006, China

    Yulei Bai

  3. Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete-Manufacturing, Guangzhou, 510006, China

    Zhaoshui He

  4. Peng Cheng Laboratory, Shenzhen, 518055, China

    Jun Lu

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  1. Zhijie Lin
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  2. Zhaoshui He
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Correspondence to Zhaoshui He.

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Lin, Z., He, Z., Wang, P. et al. SNRNet: A Deep Learning-Based Network for Banknote Serial Number Recognition. Neural Process Lett 52, 1415–1426 (2020). https://doi.org/10.1007/s11063-020-10313-9

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  • DOI: https://doi.org/10.1007/s11063-020-10313-9

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