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Pattern Analysis and Applications

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11.07.2023 | Theoretical Advances

An effective DeepWINet CNN model for off-line text-independent writer identification

verfasst von: Abderrazak Chahi, Youssef El-merabet, Yassine Ruichek, Raja Touahni

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2023

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Abstract

Writer identification based on handwriting recognition is considered one of the most common research areas in pattern recognition and biometrics. It has attracted much attention in recent decades due to the urgent need to develop biometric systems for many security applications. In this paper, Deep Writer Identification Network (DeepWINet), an effective deep Convolutional Neural Network (CNN) for writer identification, is proposed. The proposed model is evaluated in two different ways. In the first scenario, DeepWINet’s CNN activation features, computed from the connected components of the writing, are passed to a customized nearest neighbor classifier for writer identification. In the second scenario, DeepWINet is evaluated as an end-to-end CNN network where the predicted results are averaged using an efficient strategy, Score Averaging Component-Decision Combiner. The proposed approach achieves competitive or the highest State-Of-The-Art performance on eight challenging handwritten databases with different languages.

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Code: https://github.com/githubharald/WordSegmentation.

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Titel: An effective DeepWINet CNN model for off-line text-independent writer identification
verfasst von: Abderrazak Chahi
Youssef El-merabet
Yassine Ruichek
Raja Touahni
Publikationsdatum: 11.07.2023
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 3/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-023-01186-4

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