Sensors, Vol. 19, Pages 4370: Dual Model Medical Invoices Recognition

Sensors, Vol. 19, Pages 4370: Dual Model Medical Invoices Recognition Sensors doi: 10.3390/s19204370 Authors: Fei Yi Yi-Fei Zhao Guan-Qun Sheng Kai Xie Chang Wen Xin-Gong Tang Xuan Qi 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...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research