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Convolutional Neural Networks for Finance Image Classification

  • Xingjie Zhu
  • Yan Liu
  • Xingwang Liu
  • Chi Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

In recent years, deep convolutional neural networks have demonstrated excellent performance on visual tasks, such as image classification. Based on the deep architecture, this paper designs a new method to handle an automatic financial recognition problem, namely to recognize the value-added tax (VAT) invoices, finance-related documents, and other finance-related images. The proposed method consists of three steps: first, image preprocessing will be performed on the original image and the augmented image will be separated into four patches for further processing; thus the obtained image patches will be the input of a deep convolutional neural model for the training purpose; at the final step, we use the four predications which obtained from the previous step to determine the final categorizes. Our experimental result shows that this method can conduct finance image classification with high performance.

Keywords

Image classification Segmentation Neural networks 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Taikang Insurance GroupBeijingChina

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