Transfer Learning for Word Spotting in Historical Arabic Documents Based Triplet-CNN

Abir Fathallah, Abir Fathallah, Mounim El-Yacoubi, Najoua Ben Amara

2023

Abstract

With the increasing number of digitized historical documents, information processing has become a fundamental task to exploit the information contained in these documents. Thus, it is very significant to develop efficient tools in order to analyze and recognize them. One of these means is word spotting which has lately emerged as an active research area of historical document analysis. Various techniques have been suggested successfully to enhance the performance of word spotting systems. In this paper, an enhanced word spotting approach for historical Arabic documents is proposed. It involves improving learning feature representations that characterize word images. The proposed approach is mainly based on transfer learning. More precisely, it consists in building an embedding space for word image representations from an online training triplet-CNN, while performing transfer learning by leveraging the varied knowledge acquired from two different domains. The first domain is Hebrew handwritten documents, the second is English historical documents. We will investigate the impact of each domain in improving the representation of Arabic word images. As a final step, in order to evolve the word spotting system, the query word image along with all the reference word images will be projected into the embedding space where they will be matched according to their embedding vectors. We evaluate our method on the historical Arabic VML-HD dataset and show that our method outperforms significantly the state-of-the-art methods.

Paper Citation

in Harvard Style

Fathallah A., El-Yacoubi M. and Ben Amara N. (2023). Transfer Learning for Word Spotting in Historical Arabic Documents Based Triplet-CNN. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 520-527. DOI: 10.5220/0011639800003417

in Bibtex Style

@conference{visapp23,
author={Abir Fathallah and Mounim El-Yacoubi and Najoua Ben Amara},
title={Transfer Learning for Word Spotting in Historical Arabic Documents Based Triplet-CNN},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={520-527},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011639800003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Transfer Learning for Word Spotting in Historical Arabic Documents Based Triplet-CNN
SN - 978-989-758-634-7
AU - Fathallah A.
AU - El-Yacoubi M.
AU - Ben Amara N.
PY - 2023
SP - 520
EP - 527
DO - 10.5220/0011639800003417
PB - SciTePress