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A deep learning framework for historical manuscripts writer identification using data-driven features

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A Correction to this article was published on 06 March 2024

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Abstract

Writer identification form historical manuscripts presents a challenging problem with significant implications for understanding the authorship of ancient texts. In this paper, we propose a novel deep learning framework tailored for the task of historical manuscripts writer identification. Our approach leverages data-driven features, harnessing the power of neural networks to extract and learn discriminative patterns from handwritten historical documents. The key innovation of our framework lies in its ability to automatically discover and utilize relevant features from data to profile the writer, eliminating the need for manual feature engineering. Our methodology encompasses three well-defined steps: initially, manuscript preprocessing involves image denoising using advanced techniques such as non-local means and total-variation, followed by binarization using a Canny-edge detector. In the subsequent phase, we employ Harris corner detector for automatic key-point detection and clustering, allowing us to identify the regions of interest within the documents. Lastly, the features extracted from these regions are subjected to classification through transfer learning, utilizing a deep learning-based model specifically trained on the extracted patches. To achieve the final document-level identification, we enhance the system accuracy by implementing a majority vote scheme, where the aggregated decisions from multiple patches contribute to the ultimate classification outcome. We validate our approach on “ICDAR 2017” dataset, spanning different periods and writing styles of historical manuscripts. Experimental results demonstrate the superior performance of our method in accurately identifying the authors of historical documents, surpassing existing techniques. Moreover, our framework exhibits robustness in scenarios where limited training data is available. This work not only contributes to the field of historical manuscripts analysis but also highlights the potential of deep learning in solving intricate problems in the realm of document analysis and authorship attribution. Our framework offers a promising avenue for scholars and historians to gain deeper insights into the authors of historical texts, opening new doors for historical research and preservation.

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Data availability

Publicly available datasets that have been used in this paper: Icdar2017 competition on historical document writer identification (historical-wi). In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) [31].

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Acknowledgements

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of education in in Saudi Arabia for funding this research work through the project number: 444-9-507

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Authors and Affiliations

  1. Laboratory of mathematics, informatics and systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa, Algeria

    Akram Bennour & Merouane Boudraa

  2. Xynoptik Pty Limited., Victoria, Australia

    Imran Siddiqi

  3. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Mohammed Al-Sarem & Fahad Ghabban

  4. Department of Management Information System, College of Business Administration, Taibah University, Medina, Saudi Arabia

    Mohammed Al-Shabi

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  1. Akram Bennour
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  4. Mohammed Al-Sarem
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Correspondence to Akram Bennour.

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The original online version of this article was revised: The fourth and fifth author names contain errors in the original publication of this article. Mohammad Al-Sarem should be Mohammed Al-Sarem. Mohammad Al-Shaby should be Mohammed Al-Shabi.

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Bennour, A., Boudraa, M., Siddiqi, I. et al. A deep learning framework for historical manuscripts writer identification using data-driven features. Multimed Tools Appl 83, 80075–80101 (2024). https://doi.org/10.1007/s11042-024-18187-y

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