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Deep Learning for Optical Character Recognition and Its Application to VAT Invoice Recognition

机译:深度学习光学字符识别及其在增值税发票识别中的应用

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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)被认为是长期和热门研究主题之一,因为OCR技术可以通过始终生长来将文档从纸张更改为计算机可读格式。然而,目前OCR技术的识别准确性是改进一些特殊应用,例如报销增值税(增值税)发票。本文采用深卷积神经网络(CNN)和残差网络(Reset)提出了两个OCR技术。根据我们的测试数据集,前面提出的技术可以达到97.08%,后者可以增加到99.38%。

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