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SPEdu: A Toolbox for Processing Digitized Historical Documents

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Advances in Computational Intelligence (MICAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12469))

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Abstract

Historical-educational documentary sources have gained considerable attention in educational contexts. However, some sources suffer from serious problems such as inadequate infrastructure, poor preservation, and lack of qualified personnel. In addition, a large part of documents is not digitilized, making research difficult. As a consequence, there is a need for transcription, digitalization, and cataloging sources of information for the analysis of large volumes of data. To deal with this issue, we present SPEdu, a tool to digitalize sources of information demanded by research on the History of Education. The workflow of SPEdu is divided into three steps. Firstly, SPEdu adquires images from an information source. Secondly, the tool prepocesses the images and extracts features from them. Finally, a supervised machine learning module was built to classify images between text and non-text. To assess the viability of SPEdu, we used the Official Gazette of the State of Sergipe. Regarding the third step, we evaluated the performance of classification algorithms, such as J48, Logistic Regression, Multi-layered Perceptron (MLP), Naive Bayes, Random Forest, and Random Tree. Results have revealed that Random Forest outperformed remaining techniques with an average rate of 95\(\%\) of accuracy.

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Author information

Authors and Affiliations

  1. Universidade Tiradentes, Aracaju, Sergipe, Brazil

    Fabio Gomes Rocha

  2. Instituto de Tecnologia e Pesquisa - ITP, Aracaju, Sergipe, Brazil

    Fabio Gomes Rocha

  3. ISISTAN (UNICEN-CONICET) Research Institute, Tandil, Buenos Aires, Argentina

    Guillermo Rodriguez

Authors
  1. Fabio Gomes Rocha
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  2. Guillermo Rodriguez
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Corresponding author

Correspondence to Guillermo Rodriguez .

Editor information

Editors and Affiliations

  1. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Lourdes Martínez-Villaseñor

  2. Universidad Autónoma Metropolitana, Mexico City, Mexico

    Oscar Herrera-Alcántara

  3. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Hiram Ponce

  4. Universidad Autónoma del Estado de Hidalgo, Hidalgo, Mexico

    Félix A. Castro-Espinoza

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Gomes Rocha, F., Rodriguez, G. (2020). SPEdu: A Toolbox for Processing Digitized Historical Documents. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_32

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  • DOI: https://doi.org/10.1007/978-3-030-60887-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60886-6

  • Online ISBN: 978-3-030-60887-3

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