Recognizing Arabic handwritten literal amount using Convolutional Neural Networks
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Date
2022-01-24
Journal Title
Journal ISSN
Volume Title
Publisher
University of Eloued جامعة الوادي
Abstract
Currently, deep learning techniques have become the core of recent research in pattern recognition
domain and especially for the handwriting recognition field where the challenges for the Arabic language are
stilling. Despite their high importance and performances, for the best of our acknowledge, deep learning
techniques have not been investigated in the context of Arabic handwritten literal amount recognition. The
main aim of this paper is to investigate the effect of several Convolutional Neural Networks CNNs based on the
proposed architecture with regularization parameters for such context. To achieve this aim, the AHDB database
was used where very promising results were obtained outperforming the previous works on this database.
Description
Forum Intervention of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloued
Keywords
Arabic handwriting · Literal Amount Recognition · Offline recognition · deep learning · Resnet VGG
Citation
KORICHI, Aicha • SLATNIA, Sihem. TAGOUGUI, Najiba. ZOUARI, Ramzi • KHERALLAH, Monji • AIADI, Oussama. Recognizing Arabic handwritten literal amount using Convolutional Neural Networks. Forum of Artificial Intelligence and Its Applications. 24-26 Jan 2022. Faculty of Exat science. University of Eloued. [visited in ../../….]. available from [copy the link here]