## 6 Discussion and Conclusion  
Handwritten numerals recognition is an active research area which always needs an improvement in accuracy. The proposed model is a modified version from AOCT-Net [42] where the input size is 32 \(\times\) 32, and the number of classes of the fully connected (FC) layer is 10, in order to make the proposed model more efficient and suitable to the data. AOCTNet [42] previously showed that it is very simple and efficient. A simpler model means requiring less resources for both training and testing stages, and it makes the model suitable to be designed in embedded systems. Comparing the used model with other models shows that our proposed model has higher accuracy and requires less execution time. The architecture that was used in our model is totally different from other types \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Training Time (Hour)** &
\begin{tabular}{c} **Average Testing Time Per** \\ **Image (ms)** \\ \end{tabular} \\ \hline \multicolumn{3}{c}{Arabic/Hindi Classification} \\ \hline _[PERSON]_ & & 185 \\ (Learning Rate = 0.0001) & 72 & 185 \\ \hline _[PERSON]_ & & 150 \\ (Learning Rate = 0.01) & 64 & 105 \\ \hline _RMSProp_ & 37 & 111 \\ \hline \multicolumn{3}{c}{_SCDM_} & 40 & 103 \\ \hline \multicolumn{3}{c}{Arabic Numerals Recognition} \\ \hline _[PERSON]_ & & 110 \\ (Learning Rate = 0.0001) & 65 & 110 \\ \hline _[PERSON]_ & & 166 \\ (Learning Rate = 0.001) & 63 & 166 \\ \hline _[PERSON]_ & & 187 \\ (Learning Rate = 0.01) & 62 & 187 \\ \hline _RMSProp_ & 50 & 174 \\ \hline \multicolumn{3}{c}{_SCDM_} & 52 & 152 \\ \hline \multicolumn{3}{c}{Hindi Numerals Recognition} \\ \hline _[PERSON]_ & & 1203 \\ (Learning Rate = 0.0001) & 62 & 203 \\ \hline _[PERSON]_ & & 186 \\ (Learning Rate = 0.001) & 61 & 186 \\ \hline _[PERSON]_ & & 109 \\ (Learning Rate = 0.01) & 58 & 109 \\ \hline _SCDM_ & 49 & 134 \\ \hline _RMSProp_ & 50 & 117 \\ \hline \hline \end{tabular}
\end{table} 
Table 33: Average training time and average testing time. of architectures that have been used in literature. In [46], the authors built their own CNN with only six layers, three of them are convolutional layers, one fully connected layer, one softmax layer, and an output or classification layer. In [47], the authors utilized VGG19 [48] which is a pretrained CNN structure. It is composed from 19 levels (25 layers; 16 convolution layers, three fully connected layers, five MaxPool layers, and one SoftMax layer). They utilized it for deep features extraction. On the other hand, in [49] the authors used Deep Recurrent Convolutional Neural Network (DRCNN) [50] as a predictor model for bankruptcy, while in [51] the authors built their own CNN structure model based on Efficient Net architecture with nine levels (nine layers) to classify X-ray images into three classes (Normal, Pneumonia, and COVID-19). Table 34 shows the differences and the similarities between the proposed structure and the existing structures in literature. This paper utilized the benefits of deep learning algorithms with various learning rate optimizations methods in discriminating of handwritten images between Hindi and Arabic numerals alongside recognizing the numerals to their 10 classes either in Hindi or in Arabic format. The purpose of employing deep learning was to take advantages of the power of CNNs that can manage input data with large dimensions and share their weights. The three scenarios (Arabic-Hindi classification, Hindi numerals recognition, and Arabic numerals recognition) have exploited various known optimization methods; _Adam_, _RMSprop_, and _SGDM_. _Adam_ shows highly accurate recognition rate in all scenarios alongside requiring more time for processing than others. It was because _Adam_ is providing a combined heuristics optimization of both Stochastic Gradient Descent with Momentum and Root Mean Square Propagation. The proposed recognition approach has two advantages.