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Link to original content: https://doi.org/10.1007/s11042-022-12673-x
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A deep learning based system for writer identification in handwritten Arabic historical manuscripts

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Abstract

Determining the writer or transcriber of historical Arabic manuscripts has always been a major challenge for researchers in the field of humanities. With the development of advanced techniques in pattern recognition and machine learning, these technologies have been applied to automate the extraction of paleographical features in order to solve this issue. This paper presents a baseline system for writer identification, tested on a Historical Arabic dataset of 11610 single and double folio images. These texts were extracted from a unique collection of 567 Historical Arabic Manuscripts available at the Balamand Digital Humanities Center. A survey has been conducted on the available Arabic datasets and previously proposed techniques and algorithms. The Balamand dataset presents an important challenge due to the geo-historical identity of manuscripts and their physical conditions. An advanced Deep Learning system was developed and tested on three different Latin and Arabic datasets: ICDAR19, ICFHR20 and KHATT, before testing it on the Balamand dataset. The system was compared with many other systems and it has yielded a state-of-the-art performance on the new challenging images with 95.2% mean Average Precision (mAP) and 98.1% accuracy.

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Notes

  1. Mention the Arabic corpora in the domain of OCR and handwritten recognition.

  2. These manuscripts were digitized by the Saint Joseph of Damascus Manuscript Conservation Center (http://www.balamandmonastery.org.lb/index.php/about-the-center) and the Digital Humanities Centre (http://iohanes.uob-dh.org/?q=en/tags/digital-humanities).

  3. The total number of digitized pages exceeds the number of photos.

  4. “A statement providing information regarding the date, place, agency, or reason for production of the manuscript or other object” [29]

  5. A frame made of cardboard or occasionally of wood on which cords of various thickness could be stretched, corresponding to the text frame lines and guidelines [17].

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Acknowledgements

This research is funded by the EIPHI Graduate School (contract “ANR-17-EURE-0002”). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro RTX 6000 GPU used for this research.

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Authors and Affiliations

  1. Digital Humanities Center, University of Balamand, El-Koura, Lebanon

    Michel Chammas & Elie Dannaoui

  2. Femto-ST Institute, UMR CNRS 6174, Université de Bourgogne Franche-Comté, Montbéliard, France

    Michel Chammas & Abdallah Makhoul

  3. LaRRIS, Faculty of Sciences, Lebanese University, Fanar, Lebanon

    Jacques Demerjian

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  1. Michel Chammas
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  2. Abdallah Makhoul
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  3. Jacques Demerjian
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  4. Elie Dannaoui
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Correspondence to Michel Chammas.

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Chammas, M., Makhoul, A., Demerjian, J. et al. A deep learning based system for writer identification in handwritten Arabic historical manuscripts. Multimed Tools Appl 81, 30769–30784 (2022). https://doi.org/10.1007/s11042-022-12673-x

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