增值税发票信息结构化识别

Structural Information Recognition of VAT Invoice

为进一步简化增值税发票识别流程和和提高识别效率,提出了一种基于HRNet和YOLOv4的增值税票面信息结构化识别的方法.首先利用HRNet进行增值税发票关键点检测,进行增值税发票对齐;其次利用YOLOv4进行发票元素的检测;然后通过CRNN对发票元素进行文本识别;最后形成结构化数据.在业务数据集中的实验表明,检测准确率在0.5 mAP下达到75.7,检测速度达到12.85 fps,元素识别率ECR达到69.30%,实验结果表明算法能有效简化识别流程,提高识别准确率,在实时性要求较高和业务噪声复杂的增值税票据识别中有较好适应性和广泛应用前景.

To simplify the processing steps of VAT invoices and improve recognition accuracy,we propose a method based on HRNet and YOLOv4 to extract structural information of VAT invoices.Firstly,we detect predefined keypoints in the VAT invoice with the HRNet method to align the invoice to a standard template.Then detect the structural information cell in the invoice by YOLOv4.And lastly use CRNN to recognize the cell block image to obtain structural data.The experimental results on real business VAT invoices show that the proposed method gets a detection accuracy of75.7 at 0.5 mAP,reaches a detection speed at 12.85 fps,and achieves an Element Correct Ratio(ECR)at 69.30%.The results indicate that the proposed method can simplify the process and improve the accuracy of recognition,and it can apply to the scene where requires high real-time performance and needs to deal with complicated noise situation.