## 2 Background and State-of-the-Art Research  
In this section, we review the latest published methodologies in the field of numeral recognition focusing on the recognition of handwritten numerals. In our survey on related work about handwritten numeral recognition, we encountered few studies employing various classification techniques. Here, we briefly review the most recent research of this active research area, which plays an important role in the field of artificial intelligence. In [12], handwritten Arabic (Indian) numerals were classified using a two-stage classification technique with Nearest Mean (NMC), K-Nearest Neighbor (K-NNC), and Hidden Markov Models (HMMC) being used in the first stage and a Structural Classifier (SC) being applied in the second stage. The achieved recognition ratios were around 98%, on average. In their work [13], [PERSON] et al. used a supervised learning technique based on the artificial neural network (ANN) for offline handwritten numeral recognition, where they employed a multilayered perceptron (MLP) with one hidden layer. The drawback of this work is the small-sized data set with very few samples. The authors of [14] applied Adaboost on an ensemble of MLP classifiers for the recognition of three handwritten Indian numerals: Devanagari, Bangla, and Oriya. The work in [15] focused on recognizing the seven segments display of LCDs with an obtained recognition accuracy of 79%. Shape context descriptor methodology was used in [16] to recognize Marathi and English characters and numerals. The achieved recognition accuracy was between 83% and 95%. The study in [17] employed deep learning in the recognition of handwritten Arabic numerals and achieved an accuracy of 97.4%. In [18], the authors used Spiking neural networks (SNNs) in the recognition of handwritten numerals, where they were able to improve the recognition accuracy compared to the use of BP neural networks. The authors of [19] employed deep learning techniques for the recognition of numerals from multiple languages including Eastern Arabic, Persian, Devanagari, Urdu, Western Arabic, where they were able to achieve high recognition rates. The author of [20] adopted the restricted Boltzmann machine (RBM) in his work as the representative deep learning methodology for the recognition of English and Devanagari numerals with an SVM-based classifier. In [21], the authors surveyed the most-recent techniques for the recognition of printed and handwritten numerals and characters of both Indic and non-Indic scripts, where the non-Indic scripts include Bangla, Devanagari, Gurmukhi, Kannada, Oriya, and Tamil. The focus of the study in [22] was on the segmentation of a string of digits into individual numerals as the authors applied a combination of PCA and SVM classifiers and called it PCA-SVMset in the recognition of strings of unknown lengths. The technique applied in [23] combined low-level stroke (LLS) and polar histogram for the recognition of handwritten Gujarati, Devanagari, and English numerals. Nevertheless, we noticed certain shortcomings in each of previous studies including the lack of a comprehensive data set with large enough samples written by a large number of people for both Indian and Arabic numerals, in addition to unsatisfactory obtained recognition rates. Deep learning is a promising path toward a more capable artificial intelligence that has proved a success in various pattern recognition disciplines. In this study, we address these issues and present a state-of-the-art recognition approach based on deep learning methods. Researchers and technologists are witnessing the widespread popularity of deep learning theoretically and practically. Deep learning is a branch of artificial neural networks (ANNs), where deep layers are adopted instead of shallow networks used previously in ANNs. We refer interested readers to several survey papers in the field [24; 25; 26; 27; 28], where the authors of [24] survey technologies, learning mechanisms, platforms, and applications of deep learning.