A Bag-of-Words Approach for Information Extraction from Electricity Invoices
In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extract...
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| Format: | Article |
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MDPI AG
2024-10-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/4/91 |
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| author | Javier Sánchez Giovanny A. Cuervo-Londoño |
| author_facet | Javier Sánchez Giovanny A. Cuervo-Londoño |
| author_sort | Javier Sánchez |
| collection | DOAJ |
| description | In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer 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 relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model 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 unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices. |
| format | Article |
| id | doaj-art-9d5488d02be14b0aaca361a5c511a5ec |
| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-9d5488d02be14b0aaca361a5c511a5ec2025-08-20T02:53:34ZengMDPI AGAI2673-26882024-10-01541837185710.3390/ai5040091A Bag-of-Words Approach for Information Extraction from Electricity InvoicesJavier Sánchez0Giovanny A. Cuervo-Londoño1Centro de Tecnologías de la Imagen (CTIM), Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES), 3507 Las Palmas de Gran Canaria, SpainCentro de Tecnologías de la Imagen (CTIM), Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES), 3507 Las Palmas de Gran Canaria, SpainIn the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer 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 relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model 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 unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices.https://www.mdpi.com/2673-2688/5/4/91electricity invoiceinformation extractionsemi-structured documentmachine learningsupport vector machine |
| spellingShingle | Javier Sánchez Giovanny A. Cuervo-Londoño A Bag-of-Words Approach for Information Extraction from Electricity Invoices AI electricity invoice information extraction semi-structured document machine learning support vector machine |
| title | A Bag-of-Words Approach for Information Extraction from Electricity Invoices |
| title_full | A Bag-of-Words Approach for Information Extraction from Electricity Invoices |
| title_fullStr | A Bag-of-Words Approach for Information Extraction from Electricity Invoices |
| title_full_unstemmed | A Bag-of-Words Approach for Information Extraction from Electricity Invoices |
| title_short | A Bag-of-Words Approach for Information Extraction from Electricity Invoices |
| title_sort | bag of words approach for information extraction from electricity invoices |
| topic | electricity invoice information extraction semi-structured document machine learning support vector machine |
| url | https://www.mdpi.com/2673-2688/5/4/91 |
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