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2024 | OriginalPaper | Buchkapitel

Enhancing Writer Identification with Local Gradient Histogram Analysis

verfasst von : Abdelillah Semma, Said Lazrak, Yaâcoub Hannad

Erschienen in: Innovations in Smart Cities Applications Volume 7

Verlag: 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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Vorheriges Kapitel A Synthesis on Machine Learning for Credit Scoring: A Technical Guide

Nächstes Kapitel Solving a Generalized Network Design Problem Using Hybrid Metaheuristics

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Titel: Enhancing Writer Identification with Local Gradient Histogram Analysis
verfasst von: Abdelillah Semma
Said Lazrak
Yaâcoub Hannad
Verlag: Springer Nature Switzerland
Buch: Innovations in Smart Cities Applications Volume 7
Print ISBN: 978-3-031-54375-3

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

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

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