Recognizing Arabic handwritten literal amount using Convolutional Neural Networks
KORICHI, Aicha, SLATNIA, Sihem, TAGOUGUI, Najiba, ZOUARI, Ramzi, KHERALLAH, Monji, AIADI, Oussama
Résumé
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.
Détails
| Langue originale | English |
|---|---|
| Événement | Forum of Artificial Intelligence and Its Applications |
| Statut de publication | Publié — janvier 2022 |