A two-stage approach for table extraction in invoices

TL;DR

This paper presents a two-stage method combining image processing and graph-based techniques to accurately extract complex tables from invoices, improving automated document analysis.

Contribution

It introduces a novel hybrid approach that integrates shape estimation and graph modeling for precise table extraction in invoices.

Findings

Effective extraction of complex tables demonstrated

Improved accuracy over existing methods

Validated on real-world invoice data

Abstract

The automated analysis of administrative documents is an important field in document recognition that is studied for decades. Invoices are key documents among these huge amounts of documents available in companies and public services. Invoices contain most of the time data that are presented in tables that should be clearly identified to extract suitable information. In this paper, we propose an approach that combines an image processing based estimation of the shape of the tables with a graph-based representation of the document, which is used to identify complex tables precisely. We propose an experimental evaluation using a real case application.