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2024 | OriginalPaper | Chapter

Enhancing Writer Identification with Local Gradient Histogram Analysis

Authors : Abdelillah Semma, Said Lazrak, Yaâcoub Hannad

Published in: Innovations in Smart Cities Applications Volume 7

Publisher: Springer Nature Switzerland

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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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Title: Enhancing Writer Identification with Local Gradient Histogram Analysis
Authors: Abdelillah Semma
Said Lazrak
Yaâcoub Hannad
Publisher: Springer Nature Switzerland
Book: Innovations in Smart Cities Applications Volume 7
Print ISBN: 978-3-031-54375-3

Electronic ISBN: 978-3-031-54376-0

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-54376-0_10

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