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首页> 外文会议>International conference on communications, signal processing, and systems >Deep Learning for Optical Character Recognition and Its Application to VAT Invoice Recognition
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Deep Learning for Optical Character Recognition and Its Application to VAT Invoice Recognition

机译：光学字符识别的深度学习及其在增值税发票识别中的应用

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摘要

Optical character recognition (OCR) is considered as one of long-term and hot research topics due to the fact that OCR technique can change the documents from paper to computer-readable format by consistently growing. However, the recognition accuracy of current OCR technique is required to improve some special applications such as in reimbursement of value-added tax (VAT) invoices. This paper proposes two OCR techniques by using deep convolutional neural network (CNN) and residual network (ResNet), respectively. According to our test dataset, the formerly proposed techniques can reach up to 97.08%, while the latter can increase to 99.38%.
机译：光学字符识别（OCR）被认为是一项长期且热门的研究课题，这是因为OCR技术可以通过持续增长将文档从纸质变为计算机可读格式。但是，当前的OCR技术的识别精度对于改进某些特殊应用（例如增值税发票（VAT）的报销）是必需的。本文分别使用深度卷积神经网络（CNN）和残差网络（ResNet）提出了两种OCR技术。根据我们的测试数据集，前者提出的技术可以达到97.08％，而后者可以提高到99.38％。

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