## 2 Background and State-of-the-Art Research
A review of the recent algorithms and architectures that enhance both training and testing in terms of accuracy and time can be found in [25]. The survey in [26] focuses on joint representation, coordinated representation, and encoder-decoder, which are the frameworks used in Deep Multimodal Representation Learning. The authors of [27] reviewed the application of deep learning in the visual content analysis in multimedia dataconsisting of images and videos. Deep learning has been extensively used in image processing applications; meanwhile, this field has also promoted new deep learning algorithms and architectures [28]. In our previous work [10; 29] we have tackled the problem of automatic numeral recognition, wherein [10] we addressed the problem of printed Arabic numerals. In that study, the correlation coefficient was utilized to find the best match between the numeral under test and a set of reference images representing the baseline for comparison. The task of handwritten numeral recognition is more challenging than that of printed counterparts. The reason for this is due to the imperfection in the handwriting of most individuals. This includes so many writing styles with very large variances in terms of size, orientation, line thickness, and overlapping, among many others. Therefore, the purpose of our next work [29] was to handle the recognition of handwritten Arabic numerals, where we employed self-organizing maps in this task. Although in our previous work [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29] we were able to achieve high recognition ratios, our mission in this research is to extend our findings and to build upon our previous results. Specifically, the objective of this work is to build a universal numeral recognizer; one that can recognize both printed and handwritten multilingual numerals. Moreover, we aim also at using the results and findings of this work in the recognition of printed and handwritten multilingual text, in general. Herein, we present a fast and high accurate methodology using a deep learning technique that exhibits very high recognition rates for the offline detection of numerals (Arabic or Hindi) then classifying of the handwritten of both languages decimal numbers for all the numerals (0-9). The same CNN architecture has been used for all stages of recognition but trained in a different dataset. The training is done using three different types of solvers (Adaptive Moment Learning Rate (_Adam_), Root Mean Square Propagation (_RMSProp_), and Stochastic Gradient Descent with Momentum (_SGDM_)). The novelty of this paper is obtaining a highly accurate, fast, and reliable system for recognition off-line numerals handwritten in either Arabic or Hindi format, that could be used as a universal system in different fields. All previous studies did not reach 100% accuracy in their work, besides, they applied their algorithms on limited datasets, while this work was applied on three types of huge datasets. This paper is organized as follows: a description of the data processing, methodology, and CNN is presented in Section 2. Section 3 explains our scenarios and provides detailed results. Section 4 provides the time requirements of the proposed methodology/scenarios. Finally, Section 5 concludes the paper.