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SVM-based writer retrieval system in handwritten document images

  • 1169: Interdisciplinary Forensics: Government, Academia and Industry Interaction
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Abstract

Digital libraries include huge amount of information that are continuously increasing with the need of storing various kinds of handwritten documents such as administrative forms, and cultural heritage manuscripts. Therefore, new emerging techniques such as writer retrieval are used to facilitate information extraction from archived documents. Basically, a writer retrieval system is composed of two main steps that are feature generation and dissimilarity measure. To achieve a robust retrieval, we propose the use of an SVM classifier trained to automatically separate intra-writer features from inter-writer features. For feature generation, we investigate the effectiveness of the Histogram of Oriented Gradients, Gradient Local Binary Patterns, and Local Difference Features. Experiments are conducted on three benchmark datasets. The results obtained evince a satisfactory performance of SVM, which can give better results than the state of the art.

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Data Availability

The used datasets are a benchmarking datasets that are available online. The CVL dataset is available on http://hwww.caa.tuwien.ac.at. The ICDAR-2011 dataset is available on: http://www.cvc.uab.es/icdar2011competition. The KHATT dataset is available on: http://khatt.ideas2serve.net/. This study uses a custom code which is only available via contacting authors.

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Authors and Affiliations

  1. Laboratoire d’Ingénierie des Systèmes Intelligents et Communicants (LISIC), Faculty of Electronics and Computer Sciences (FEI), University of Sciences and Technology Houari Boumediene (USTHB), Bab Ezzouar, Algiers, Algeria

    Mohamed Lamine Bouibed, Hassiba Nemmour & Youcef Chibani

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  1. Mohamed Lamine Bouibed
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  2. Hassiba Nemmour
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  3. Youcef Chibani
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Correspondence to Mohamed Lamine Bouibed.

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Bouibed, M.L., Nemmour, H. & Chibani, Y. SVM-based writer retrieval system in handwritten document images. Multimed Tools Appl 81, 22629–22651 (2022). https://doi.org/10.1007/s11042-020-10162-7

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  • DOI: https://doi.org/10.1007/s11042-020-10162-7

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