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Image pattern recognition in identification of financial bills risk management

  • S.I. : ATCI 2020
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The automatic identification system of financial bills needs to have high recognition rate, high anti-interference and real-time to ensure its recognition effect. Based on the image recognition theory, this study uses the differential projection method to define the boundary of the bill. The horizontal projection gradation of the boundary area of the bill character line is significantly reduced from large to small, and the horizontal difference projection of the grayscale image can be performed to locate the boundary of the bill image. The word height of bills studied in this paper is constant, the character line spacing is equal, and the horizontal differential projection is used to realize row positioning. The main process of the system is to first select the check image, and then the software part realizes the process of image analysis, preprocessing, character segmentation, feature extraction, character recognition, etc. Finally, the recognized amount is output to the interface. It is proved by experiments that the recognition rate of this algorithm is high, which can provide theoretical reference for subsequent related research.

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Authors and Affiliations

  1. College of Finance and Economics, Shandong University of Science and Technology, Tai’an, 266590, Shandong, China

    Huahua Li

  2. Department of Information Technology, China Merchants Bank, Shenzhen, 518040, China

    Chuanchao Huang

  3. School of Economics and Management, Taishan University, Tai’an, 271000, Shandong, China

    Lihan Gu

Authors
  1. Huahua Li
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  2. Chuanchao Huang
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  3. Lihan Gu
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Correspondence to Lihan Gu.

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Cite this article

Li, H., Huang, C. & Gu, L. Image pattern recognition in identification of financial bills risk management. Neural Comput & Applic 33, 867–876 (2021). https://doi.org/10.1007/s00521-020-05261-3

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  • DOI: https://doi.org/10.1007/s00521-020-05261-3

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