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Open AccessArticle

Dual Model Medical Invoices Recognition

1
Key Laboratory of Exploration Technologies for Oil and Gas Resources, Yangtze University, Ministry of Education, Wuhan 430100, China
2
School of Electronic Information, Yangtze University, Jingzhou 434023, China
3
School of Computer Science, Yangtze University, Jingzhou 434023, China
4
School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(20), 4370; https://doi.org/10.3390/s19204370
Received: 30 August 2019 / Revised: 26 September 2019 / Accepted: 3 October 2019 / Published: 10 October 2019
(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)
Hospitals need to invest a lot of manpower to manually input the contents of medical invoices (nearly 300,000,000 medical invoices a year) into the medical system. In order to help the hospital save money and stabilize work efficiency, this paper designed a system to complete the complicated work using a Gaussian blur and smoothing–convolutional neural network combined with a recurrent neural network (GBS-CR) method. Gaussian blur and smoothing (GBS) is a novel preprocessing method that can fix the breakpoint font in medical invoices. The combination of convolutional neural network (CNN) and recurrent neural network (RNN) was used to raise the recognition rate of the breakpoint font in medical invoices. RNN was designed to be the semantic revision module. In the aspect of image preprocessing, Gaussian blur and smoothing were used to fix the breakpoint font. In the period of making the self-built dataset, a certain proportion of the breakpoint font (the font of breakpoint is 3, the original font is 7) was added, in this paper, so as to optimize the Alexnet–Adam–CNN (AA-CNN) model, which is more suitable for the recognition of the breakpoint font than the traditional CNN model. In terms of the identification methods, we not only adopted the optimized AA-CNN for identification, but also combined RNN to carry out the semantic revisions of the identified results of CNN, meanwhile further improving the recognition rate of the medical invoices. The experimental results show that compared with the state-of-art invoice recognition method, the method presented in this paper has an average increase of 10 to 15 percentage points in recognition rate. View Full-Text
Keywords: medical invoices; breakpoint font; CNN; RNN; semantic revisions medical invoices; breakpoint font; CNN; RNN; semantic revisions
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MDPI and ACS Style

Yi, F.; Zhao, Y.-F.; Sheng, G.-Q.; Xie, K.; Wen, C.; Tang, X.-G.; Qi, X. Dual Model Medical Invoices Recognition. Sensors 2019, 19, 4370.

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