Journal of System Simulation
Abstract
Abstract: Automatic identification of invoices can effectively improve financial efficiency. But low-resolution invoice image reduces the accuracy of automatic identification, an ESRGAN (Encoder Super-resolution Generative Adversarial Network) network for super-resolution processing of invoice images is proposed. The ESRGAN network is based on a conditional generative adversarial network. An auxiliary encoder is designed to guide the network to generate a more realistic super-resolution image. Based on the actual invoice image, the ESRGAN network and the conventional image processing, SRCNN (Super-resolution Convolutional Neural Networks) network and SRGAN (Super-resolution Generative Adversarial Network) network. The model is evaluated through two evaluation indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results show that the images processed based on ESRGAN super-resolution are better on visual effects and evaluation indicators.
Recommended Citation
Li, Xinli; Zou, Changming; Yang, Guotian; and He, Liu
(2021)
"Research of Super-resolution Processing of Invoice Image Based on Generative Adversarial Network,"
Journal of System Simulation: Vol. 33:
Iss.
6, Article 9.
DOI: 10.16182/j.issn1004731x.joss.20-0095
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss6/9
First Page
1307
Revised Date
2020-05-02
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0095
Last Page
1314
CLC
TP391.4
Recommended Citation
Li Xinli, Zou Changming, Yang Guotian, Liu He. Research of Super-resolution Processing of Invoice Image Based on Generative Adversarial Network[J]. Journal of System Simulation, 2021, 33(6): 1307-1314.
DOI
10.16182/j.issn1004731x.joss.20-0095
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