Ensemble deep transfer learning model for Arabic (Indian) handwritten digit recognition
Alkhawaldeh, Rami S
Funkur Alshdaifat, Nawaf Farhan
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Recognising handwritten digits or characters is a challenging task due to noisy data that results from different writing styles. Numerous applications essentially motivate to build an effective recognising model for such purposes by utilizing recent intelligent techniques. However, the difficulty emerges when using the Arabic language that suffers from diverse noises; because of the way of writing inherent in connecting characters and digits. Therefore, this work focuses on the Arabic (Indian) digits and propose an ensemble deep transfer learning (EDTL) model that efficaciously detect and recognise these digits. The EDTL model is a combination of two effective pre-trained transfer learning models that consume time and cost complexity in the training phase. The EDTL is trained on large datasets to extract relevant features as input to a fully-connected Artificial Neural Network classifier. The experimental results, using popular datasets, show significant performance obtained by the EDTL model with accuracy reached up to 99.83% in comparison to baseline methods include deep transfer learning models, ensemble deep transfer learning models and state-of-the-art techniques.