图像识别和机器学习技术在支付数据处理中的应用综述与挑战

Artjoms Suponenkovs, A. Sisojevs, Guntis Mosans, Jānis Kampars, Krisjanis Pinka, J. Grabis, A. Ločmelis, R. Taranovs
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引用次数: 10

摘要

由于手工单据处理的高成本,自动收据分析问题非常重要。因此,本文对接收图像的分析问题进行了研究。介绍了收据图像预处理、收据文本检测、收据文本识别和收据文本分析的方法。这些方法可以使收据分析系统适应现实生活环境,并将输入信息转换为分析收据信息的可用格式。定义了从图像采集到支付数据发布的支付数据处理流程,并针对该流程的各个阶段提出了相应的技术。回顾了这些技术的优点和局限性,并确定了开放的研究挑战。将支付数据处理作为费用报告流程数字化转型的推动者进行分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of image recognition and machine learning technologies for payment data processing review and challenges
The automatic receipt analysis problem is very relevant due to high cost of manual document processing. Therefore, the presented paper investigates the problems of receipt image analysis. It describes approaches for receipt image pre-processing, receipt text detection, receipt text recognition and receipt text analysis. These approaches allow to make receipt analysis system adaptable for a real-life environment and to convert the input information to a usable format for analysing information in the receipts. A pipeline for payment data processing staring with image capture to payment data posting is defined and appropriate technologies for every stage of the process are proposed. Advantages and limitations of these technologies are reviewed and open research challenges are identified. The payment data processing is analyzed as an enabler of digital transformation of expense reporting processes.
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