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Few-Shot Learning for Character Recognition in Persian Historical Documents

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14505))

  • 590 Accesses

Abstract

Digitizing historical documents is crucial for the preservation of cultural heritage. The digitization of documents written in Perso-Arabic scripts, however, presents multiple challenges. The Nastaliq calligraphy can be difficult to read even for a native speaker, and the four contextual forms of alphabet letters pose a complex task to current optical character recognition systems. To address these challenges, the presented study develops an approach for character recognition in Persian historical documents using few-shot learning with Siamese Neural Networks. A small, novel dataset is created from Persian historical documents for training and testing purposes. Experiments on the dataset resulted in a 94.75% testing accuracy for the few-shot learning task, and a 67% character recognition accuracy was observed on unseen documents for 111 distinct character classes.

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Notes

  1. 1.

    https://www.loc.gov.

  2. 2.

    https://library.harvard.edu/collections/islamic-heritage-project.

  3. 3.

    Available at: https://huggingface.co/datasets/iarata/PHCR-DB25.

  4. 4.

    Available for academic purposes at https://huggingface.co/iarata/Few-Shot-PHCR.

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Authors and Affiliations

  1. College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga, 525-8577, Japan

    Alireza Hajebrahimi, Michael Evan Santoso, Mate Kovacs & Victor V. Kryssanov

Authors
  1. Alireza Hajebrahimi
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  2. Michael Evan Santoso
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  3. Mate Kovacs
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  4. Victor V. Kryssanov
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Corresponding authors

Correspondence to Alireza Hajebrahimi or Mate Kovacs .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Catania, Italy

    Giuseppe Nicosia

  2. Newcastle University, Newcastle upon Tyne, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  6. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Hajebrahimi, A., Santoso, M.E., Kovacs, M., Kryssanov, V.V. (2024). Few-Shot Learning for Character Recognition in Persian Historical Documents. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_20

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  • DOI: https://doi.org/10.1007/978-3-031-53969-5_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53968-8

  • Online ISBN: 978-3-031-53969-5

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