Arabic (Indian) digit handwritten recognition using recurrent transfer deep architecture

Abstract

The rapid volume of digit texts and images motivates researchers to build solid and efficient prediction models to recognize such media. The Arabic language is considered one of the difficult languages regarding the way of writing characters and digits. Recent research focuses on such language for building predictive approaches to recognize written materials. The Arabic (Indian) digit recognition task has been a challenging task and has gained more attention from researchers who build optimal predictive models from historical images that are used in many applications. However, transfer learning approaches exploit deep pre-trained models that could be re-used for similar tasks. So, in this paper, we propose an adapted deep hybrid transfer model developed using two well-known pre-trained convolutional neural networks (CNN) models. These are further adapted by adding recurrent neural networks especially long short-term memory (LSTM) architectures to detect Arabic (Indian) Handwritten Digits (AHD). The CNN model learns the relevant features of Arabic (Indian) digits, while the sequence learning process in the LSTM layers extracts long-term dependence features. The experimental results, using popular datasets, show significant performance obtained by the adapted transfer models with accuracy reached up to 98.92% as well as with precision and recall values at most cases reached to 100% with statistical t test using p-value ($p\le 0.05$) compared to baseline methods.