Deep learning for automatic sale receipt understanding

Rizlène Raoui-Outach, Cécile Million-Rousseau, A. Benoît, P. Lambert
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引用次数: 8

Abstract

As a general rule, data analytics are now mandatory for companies. Scanned document analysis brings additional challenges introduced by paper damages and scanning quality. In an industrial context, this work focuses on the automatic understanding of sale receipts which enable access to essential and accurate consumption statistics. Given an image acquired with a smart-phone, the proposed work mainly focuses on the first steps of the full tool chain which aims at providing essential information such as the store brand, purchased products and related prices with the highest possible confidence. To get this high confidence level, even if scanning is not perfectly controlled, we propose a double check processing tool-chain using Deep Convolutional Neural Networks (DCNNs) on one hand and more classical image and text processings on another hand. The originality of this work relates in this double check processing and in the joint use of DCNNs for different applications and text analysis.
深度学习自动销售收据理解
作为一般规则,数据分析现在对公司来说是强制性的。扫描文档分析给纸张损伤和扫描质量带来了新的挑战。在工业环境中,这项工作的重点是自动理解销售收据,从而能够获得基本和准确的消费统计数据。给定用智能手机获取的图像,建议的工作主要集中在完整工具链的第一步,旨在以尽可能高的置信度提供基本信息,如商店品牌,购买的产品和相关价格。为了获得这种高置信度,即使扫描不是完全控制的,我们提出了一个双重检查处理工具链,一方面使用深度卷积神经网络(DCNNs),另一方面使用更经典的图像和文本处理。这项工作的独创性在于这种双重检查处理和DCNNs在不同应用和文本分析中的联合使用。
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