## 4 Results
### Arabic-Hindi Classification  
In this scenario, the main goal is to classify the images under consideration into two classes: Arabic numerals or Hindi numerals. The CNN was trained using the training data that were explained previously. In this regard, we had to exclude some numbers that could be written in the same manner in both languages (Hindi: 1, 0, 0, 0, Arabic: 1, 0, 0, 0). Therefore, the total number of data used to train and test is 59,100 images, instead of 98,500 images. Three algorithms were trained and tested in this regard: _Adam_, _RMSProp_, and _SGDM_. Table 4 shows part of the results of this scenario. In this table, the recognition rate was high for all classifiers, they all show promising results. The recognition rate was close to 100%. _Adam_ is providing a combined heuristics optimization of both Stochastic Gradient Descent with Momentum and Root Mean Square Propagation, and it can build a gradient descent which combats the pathological curvature of the problem with a faster search at the same time. Table 5 shows detailed results of _Adam_ classifier, only 18 images out of 8784 images were misrecognized. The recognition rate was 99.8% (99.7% for Hindi, 99.8% for Arabic). Table 6 shows all the misrecognized images for Arabic-Hindi classification using _Adam_ training algorithm. It is obvious from this table that some of the images were misrecognized due to unclear handwriting. From both tables, it is obvious that the recognition rate of Arabic images was slightly better than the recognition rate of Hindi images. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Algorithm** \\ \end{tabular} } & \multicolumn{4}{c}{**Training Data**} & \multicolumn{2}{c}{**Testing Data**} \\ \cline{2-9}  & **Number** & **Number** & **Recognition** & **Rate** & **Hours** & **Number of** & **Number of** & **Recognition** \\  & **of Arabic** & **Images** & **(\%)** & **(Approx)** & **Images** & **Hindi** & **Rate** \\ \hline _Adam_ & 20,957 & 20,412 & 100 & 72 & 4454 & 4330 & 99.80 \\ \hline _RMSProp_ & 20,957 & 20,412 & 99.96 & 37 & 4447 & 4339 & 99.73 \\ \hline _SGDM_ & 20,957 & 20,412 & 100 & 40 & 4443 & 4347 & 99.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of results for Arabic–Hindi classification using three different training algorithms. Table 7 shows detailed results of _RMSProp_ classifier, only 23 images out of 8784 images were misrecognized. The recognition rate was 99.73% (99.65% for Hindi, 99.82% for Arabic). Table 8 shows all the misrecognized images for Arabic-Hindi classification using _RMSProp_ training algorithm. It is obvious from this table that some of the images were misrecognized due to unclear handwriting. From both tables, it is obvious that the recognition rate of Arabic images was slightly better than the recognition rate of Hindi images. Table 9 shows detailed results of _SGDM_ classifier, only 24 images out of 8784 images were misrecognized. The recognition rate was 99.73% (99.68% for Hindi, 99.77% for Arabic). Table 10 shows all the misrecognized images for Arabic-Hindi classification using _SGDM_ training algorithm. It is obvious from this table that some of the images were misrecognized due to unclear handwriting. From both tables, it is obvious that the recognition rate of Arabic images was slightly better than the recognition rate of Hindi images. Table 12 shows detailed results of _Adam_ classifier when the learning rate was 0.0001. Only 34 images out of 7384 images were misrecognized. The recognition rate was 99.54%. All numerals achieved recognition rate higher than 99%. The least recognition rate was for numeral \"4\". It was misrecognized seven times; six of them as \"9\". This happened because of the way that number was written, which is sometimes can be misleading. Table 13 shows all the misrecognized images for Arabic classification using this setup. It is obvious from this table that some of the images were misrecognized due to unclear and misleading handwriting.