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Enhancing Writer Identification with Local Gradient Histogram Analysis

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Innovations in Smart Cities Applications Volume 7 (SCA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 938))

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Abstract

Writer identification is a critical aspect of document analysis and has significant implications in various domains, including forensics, authentication, and historical research. In this article, we propose a novel approach for writer identification using gradient angle histograms collected from neighboring pixels. By calculating the histogram of gradient angles from different locations of neighboring pixels, we effectively capture the writer’s unique style and nuances. Our experimental study demonstrates promising results on the two datasets BFL and CERUG, showcasing the potential of our proposed technique in improving the state-of-the-art methods in writer identification.

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Author information

Authors and Affiliations

  1. Ibn Tofail University, Kenitra, Morocco

    Abdelillah Semma & Said Lazrak

  2. Faculty of Educational Sciences, Mohammed V University, Rabat, Morocco

    Yaâcoub Hannad

Authors
  1. Abdelillah Semma
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  2. Said Lazrak
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  3. Yaâcoub Hannad
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Corresponding author

Correspondence to Abdelillah Semma .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Fac Sciences et Techniques de Tanger, Université Abdelmalek Essaâdi, Tangier, Morocco

    Anouar Abdelhakim Boudhir

  3. École Spéciale des Travaux Publics, Paris, France

    Rani El Meouche

  4. Computer Engineering Department, Karabük University, Karabük, Türkiye

    İsmail Rakıp Karaș

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Semma, A., Lazrak, S., Hannad, Y. (2024). Enhancing Writer Identification with Local Gradient Histogram Analysis. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_10

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  • DOI: https://doi.org/10.1007/978-3-031-54376-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54375-3

  • Online ISBN: 978-3-031-54376-0

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