Arabic Handwriting off-Line Recognition Using ConvLSTM-CTC

Takwa Gader, Issam Chibani, Afef Echi

2023

Abstract

This work is released in the field of automatic document recognition, specifically offline Arabic handwritten recognition. Arabic writing is cursive and recognized as quite complex compared to handwritten Latin script: dependence on context, difficulties with segmentation, a large number of words, variations in the style of the writing, inter- and intra-word overlap, etc. Few works exist concerning recognizing Arabic manuscripts without constraint, which motivates us to move towards this type of document based on an approach based on deep learning. It is one of the machine-learning approaches reputed to be effective for classification problems. It is about conceiving and implementing an end-to-end system: a convolutional long-short-term memory (ConvLSTM ). It consists of a recurrent neural network for spatiotemporal prediction with convolutional structures that allow feature extraction. A connectionist temporal classification output layer processes the returned result. Our model is trained and tested using the IFN/ENIT database. We were able to achieve a recognition rate of 99.01%.

Paper Citation

in Harvard Style

Gader T., Chibani I. and Echi A. (2023). Arabic Handwriting off-Line Recognition Using ConvLSTM-CTC. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 529-533. DOI: 10.5220/0011794700003411

in Bibtex Style

@conference{icpram23,
author={Takwa Gader and Issam Chibani and Afef Echi},
title={Arabic Handwriting off-Line Recognition Using ConvLSTM-CTC},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={529-533},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011794700003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Arabic Handwriting off-Line Recognition Using ConvLSTM-CTC
SN - 978-989-758-626-2
AU - Gader T.
AU - Chibani I.
AU - Echi A.
PY - 2023
SP - 529
EP - 533
DO - 10.5220/0011794700003411