Attention-based CNN-ConvLSTM for Handwritten Arabic Word Extraction
Ben Aïcha, Takwa (University of Tunis. University of Sfax (Tunísia))
Echi, Afef Kacem (University of Tunis (Tunísia))

Fecha:	2022
Resumen:	Word extraction is one of the most critical steps in handwritten recognition systems. It is challenging for many reasons, such as the variability of handwritten writing styles, touching and overlapping characters, skewness problems, diacritics, ascenders, and descenders' presence. In this work, we propose a deep-learning-based approach for handwritten Arabic word extraction. We used an Attention-based CNN-ConvLSTM (Convolutional Long Short-term Memory) followed by a CTC (Connectionist Temporal Classification) function. Firstly, the text-line input image's essential features are extracted using Attention-based Convolutional Neural Networks (CNN). The extracted features and the text line's transcription are then passed to a ConvLSTM to learn a mapping between them. Finally, we used a CTC to learn the alignment between text-line images and their transcription automatically. We tested the proposed model on a complex dataset known as KFUPM Handwritten Arabic Text (KHATT \cite{khatt}). It consists of complex patterns of handwritten Arabic text-lines. The experimental results show an apparent efficiency of the used combination, where we ended up with an extraction success rate of 91. 7\%.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Attention mechanism ; ConvLSTM ; CTC ; Deep-learning ; Handwritten Arabic word extraction
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 21 Núm. 1 (2022) , p. 121-129 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1433
DOI: 10.5565/rev/elcvia.1433

14 p, 752.9 KB

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