Application of image recognition and machine learning technologies for payment data processing review and challenges

机译：图像识别和机器学习技术在支付数据处理审查和挑战中的应用

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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.

机译：由于手工文档处理的高成本，自动收据分析问题非常重要。因此，本文研究了收据图像分析的问题。它描述了收据图像预处理，收据文本检测，收据文本识别和收据文本分析的方法。这些方法允许使收据分析系统适用于现实生活环境，并将输入信息转换为可用于分析收据中信息的可用格式。定义了一个从图像捕获到支付数据过帐的支付数据处理流水线，并提出了适合该过程每个阶段的技术。回顾了这些技术的优点和局限性，并确定了开放研究的挑战。支付数据处理被分析为费用报告过程的数字化转换的促成因素。

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《IEEE Workshop on Advances in Information, Electronic and Electrical Engineering》|2017年|1-6|共6页
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Artjoms Suponenkovs; Aleksandrs Sisojevs; Guntis Mosāns; Jānis Kampars; Krišjānis Pinka; Jānis Grabis; Audris Locmelis; Romāns Taranovs;
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Image color analysis; Brightness; Text recognition; Task analysis; Text analysis; Engines; Artificial neural networks;

机译：图像色彩分析;亮度;文本识别;任务分析;文本分析;引擎;人工神经网络;

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Application of image recognition and machine learning technologies for payment data processing review and challenges

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