Automatic Recognition of Financial Instruments Based on Anisotropic Partial Differential Equations

Wenfu Pan,Ruxing Zhang,Li Chen,Miaochao Chen

doi:10.1155/2021/6529859

Abstract

In this paper, anisotropic partial differential equations are used to conduct an indepth study and analysis of automatic recognition of financial bills. Firstly, it obtains the invoices of the group enterprise, uses scanning technology and related image recognition technology to capture, process, compress and slice the paper bill content, and then carries out data identification and verification of the image. It classifies the obtained electronic data information into bills, converts it into electronic information related to bills according to the corresponding categories of the bill template, and stores it in the bill table of the database to achieve the management operation of formatted electronic files. After categorizing the bills according to the electronic information of bills to match the business scenarios, financial journal vouchers can be generated according to the preconfigured voucher templates of the corresponding business scenarios, and the financial journal vouchers are converted into voucher messages using XML technology. Finally, we use agent technology to design middleware for heterogeneous financial systems to realize the function of communicating voucher messages to each other in different business systems. The system automatically extracts the key information of invoices through OCR technology and performs real-time verification and cyclical feedback to the verification results to the suppliers. The system has realized the intelligent management of the power company’s VAT invoices, thus greatly enhancing the efficiency of VAT invoice verification and settlement. The automatic tax invoice recognition system adopts a network structured tax invoice recognition model, which eliminates the cumbersome steps of character decomposition and character classification in traditional OCR character recognition. After several trials, it has obtained better experimental results in terms of recognition accuracy, with an accuracy rate of over 93% in the recognition of tax invoice data set.

Highlights

Tax bills such as receiving and paying invoices are the basis of proving economic acts, which are the evidence that can be verified by the financial personnel of enterprises and institutions, have the functions of clarifying responsibilities and accounting for taxpayers; for the state taxation department, they are a kind of evidence for auditing and determining responsibilities [1]
This paper focuses on the requirement analysis, system design, and implementation of tax bill content recognition system, design, and implementation of the tax bill content recognition system
The basic theory and numerical algorithm of partial differential equations are first analyzed, and a high-order full variance improvement method is proposed for the problem of step effect in the classical full variance denoising model

Summary

Introduction

Tax bills such as receiving and paying invoices are the basis of proving economic acts, which are the evidence that can be verified by the financial personnel of enterprises and institutions, have the functions of clarifying responsibilities and accounting for taxpayers; for the state taxation department, they are a kind of evidence for auditing and determining responsibilities [1]. Various economic activities of individuals or enterprises and institutions will generate many tax bills, and the scale of quantity of tax bills has increased dramatically. The prosperity of modern industry, commerce, and daily economic activities has prompted the frequent use of invoices and other tax documents to be urgently recorded and kept by information technology, which means that the work of regular manual processing and management of tax documents is handed over to calculation and completion. The traditional way of manually entering the information of tax bills into the computer system cannot meet the requirements of people

Objectives

Results

Conclusion