An Efficient Deep Learning-Based Approach to Automating Invoice Document Validation

TL;DR

This paper proposes to automate the validation of machine written invoices using document layout analysis and object detection techniques based on recent deep learning (DL) models, and introduces a novel dataset consisting of manually annotated real-world invoices and a multi-criteria validation process.

Abstract

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.