Information Extraction from Invoices

Hamdi, Ahmed; Carel, Elodie; Joseph, Aurélie; Coustaty, Mickaël; Doucet, Antoine

doi:10.1007/978-3-030-86331-9_45

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…Some typical examples [17] relied on Long Short-term Memory (LSTM) neural networks [18,19]. The method proposed in [20] compared two deep learning approaches, the first based on an NER strategy using a context of words around each label, and the second based on a set of features, similar to those of CloudScan [21]. This second method provided similar results with less data for training.…”

Section: Related Workmentioning

confidence: 99%

A Bag-of-Words Approach for Information Extraction from Electricity Invoices

Sánchez,

Cuervo-Londoño

2024

Preprint

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In an era marked by digitization and automation, extracting relevant information from business documents, like electricity invoices, remains a major challenge. To address this, we need to use machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new method for extracting key values from this type of invoice, including customer personal data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach uses a bag-of-words strategy and custom-designed features specifically tailored for electricity data. We tested the method on the IDSEM dataset, which includes 75.000 electricity invoices with eighty-six different fields. The method converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of the model on unseen documents. The precision obtained with Support Vector Machines is 91,86%, peaking at 98,47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices.

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Section: Related Workmentioning

confidence: 99%

A Bag-of-Words Approach for Information Extraction from Electricity Invoices

Sánchez,

Cuervo-Londoño

2024

Preprint

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“…Experiments by Hamdi et al [32] with invoice information extraction of document type and number, dates, amounts, and currency show that word classification approach (similar to [18]) outperforms sequence labelling methods [31] on most fields.…”

Section: Related Workmentioning

confidence: 99%

Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features

Ha,

Horák

2022

Preprint

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While storing invoice content as metadata to avoid paper document processing may be the future trend, almost all of daily issued invoices are still printed on paper or generated in digital formats such as PDFs. In this paper, we introduce the OCRMiner system for information extraction from scanned document images which is based on text analysis techniques in combination with layout features to extract indexing metadata of (semi-)structured documents. The system is designed to process the document in a similar way a human reader uses, i.e. to employ different layout and text attributes in a coordinated decision. The system consists of a set of interconnected modules that start with (possibly erroneous) character-based output from a standard OCR system and allow to apply different techniques and to expand the extracted knowledge at each step. Using an open source OCR, the system is able to recover the invoice data in 90% for English and in 88% for the Czech set.

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“…Next comes the information extraction phase, which entails identifying the various identifiers such as types, amounts, dates, and other crucial details from the invoices. To achieve this, natural language processing (NLP) techniques, such as named entity recognition (NER), are typically employed, which aids in recognizing and extracting specific information from the text [50], [52].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Data Extraction From Invoices

Saout,

Lardeux,

Saubion

2024

IEEE Access

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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.

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Information Extraction from Invoices

Cited by 12 publications

References 15 publications

A Bag-of-Words Approach for Information Extraction from Electricity Invoices

A Bag-of-Words Approach for Information Extraction from Electricity Invoices

Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features

An Overview of Data Extraction From Invoices

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