用深度学习自动验证发票，支持手写和手机拍照文档。
An Efficient Deep Learning-Based Approach to Automating Invoice Document Validation
- 结合文档布局分析与目标检测，实现多条件发票验证。
- 在真实发票数据集上，验证准确率高且处理速度快。
- 适合需要自动化财务审核的企业或系统开发者。
大型组织中财务交易量迅速增长，亟需快速准确的多条件发票验证。人工处理易出错且耗时，现有自动化方案难以应对手写或手机拍摄等复杂场景。本文提出基于深度学习的发票自动化验证方法，利用文档布局分析与目标检测技术，构建包含真实世界发票的标注数据集，并设计多条件验证流程。对主流深度学习模型进行微调与基准测试。实验表明，所提方法在真实数据上可实现高效准确的发票验证。
原文摘要 · Abstract (English)
In large organizations, the number of financial transactions can grow rapidly, driving the need for fast and accurate multi-criteria invoice validation. Manual processing remains error-prone and time-consuming, while current automated solutions are limited by their inability to support a variety of constraints, such as documents that are partially handwritten or photographed with a mobile phone. In this paper, we propose to automate the validation of machine written invoices using document layout analysis and object detection techniques based on recent deep learning (DL) models. We introduce a novel dataset consisting of manually annotated real-world invoices and a multi-criteria validation process. We fine-tune and benchmark the most relevant DL models on our dataset. Experimental results show the effectiveness of the proposed pipeline and selected DL models in terms of achieving fast and accurate validation of invoices.
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