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This work was funded by the Norwegian Research Council grant 329745 Machine Teaching for Explainable AI, CIPROM/2022/6 (FASSLOW) funded by Generalitat Valenciana, the EC H2020-EU grant agreement No. 952215 (TAILOR), and Spanish grant PID2021-122830OB-C42 (SFERA) funded by MCIN/AEI/10.13039/501100011033 and "ERDF A way of making Europe". Authors thank the Catedra de Inteligencia Artificial aplicada a la Administracion Pblica of Universitat Politecnica de Valencia (UPV).

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Automatic PDF Document Classification with Machine Learning

Publicat a:Lecture Notes In Computer Science. 15346 447-459 - 2025-01-01 15346(), DOI: 10.1007/978-3-031-77731-8_40

Autors: Llacer Luna, Socrates; Garigliotti, Dario; Martinez Plumed, Fernando; Ferri Ramirez, Cesar

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Resum

Universitat Polit`ecnica de Val`encia (UPV) faces challenges in managing its Alfresco document repository, which contains 600,000 PDF files, of which only 100,000 are correctly categorised. Manual classification is laborious and error-prone, hindering information retrieval and advanced search capabilities. This project presents an automated pipeline that integrates optical character recognition (OCR) and machine learning to efficiently classify documents. Our approach distinguishes between scanned and digital documents, accurately extracts text and categorises it into 51 predefined categories using models such as BERT and RF. By improving document organisation and accessibility, this work optimises UPV's document management and paves the way for advanced search technologies and real-time classification systems.

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