An Overview of Data Extraction from Invoices
Résumé
This paper provides a comprehensive overview of the process for information retrieval
from invoices. Invoices serve as proof of purchase and contain important information, including the
date, description, quantity, and the price of goods or services, as well as the terms of payment.
Companies must process invoices quickly and accurately to maintain proper financial records. To
automate this workflow, commercial systems have been developed. Despite the complexity involved,
realizing automated processing of invoices necessitates the harmonious integration of a wide range of
techniques and methods. While several surveys have shed light on different aspects of this workflow,
our objective in this paper is to present a synthetic view of the process and emphasize the most
pertinent challenges. We discuss the digitalization of invoices and the use of natural language
processing techniques to extract relevant information. We also review machine learning and deep
learning techniques that are widely used to handle the variability of layouts, minimize end-user
tasks, and train and adapt to new contexts. The purpose of this overview is not to evaluate various
systems and algorithms, but rather to propose a survey that reviews a wide scope of techniques for
different data extraction tasks, addressing both information extraction and structure recognition
for invoice processing. Specifically, we focus on table processing, paying particular attention to
graph-based approaches.