# Towards Accurate Children's Arabic Handwriting Recognition via Deep Learning
## 4 Results and Discussion
### Results  
The goal of our research was to improve the accuracy of children's handwriting recognition. We used the Hijja dataset to evaluate the performance of the proposed CNN models. Two experiments were conducted. The first one was to classify the dataset into 29 classes, the number of letters in the alphabet including _hamza_. The second was to classify the dataset into 108 categories, the number of letters in the alphabet in all their separate and connected forms. All the models were trained and tested on the Hijja dataset, and all the models were designed for multiclass classification of the alphabet into 29 or 108 labels.  
4.2.1. Results for the 29 Classes (CNN and VGG-16)  
Our aim was to demonstrate how the CNN and VGG-16 performed after being trained with the Hijia. Table 3 compares their performance measures. Regarding the classification accuracy, precision, recall, and F1-score, the CNN performed better than the VGG-16, achieving an accuracy value of 99% on the testing set. Table 3 also shows that all class numbers from 0 to 27 corresponded to the alphabet in order from _alif_ to _ya_, while classification number 28 corresponded to _hamza_.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Model** & & **CNN** & & & **VGG-16** & \\ \hline
**Character** & **Class** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline \(\uparrow\) & **0** & 1.00 & 1.00 & 1.00 & 0.96 & 0.95 & 0.96 \\ \(\backsim\) & **1** & 0.99 & 1.00 & 0.99 & 0.78 & 0.91 & 0.84 \\ \(\backsim\) & **2** & 0.99 & 0.99 & 0.99 & 0.92 & 0.81 & 0.86 \\ \(\backsim\) & **3** & 1.00 & 1.00 & 1.00 & 0.92 & 0.89 & 0.90 \\ \(\bigtriangledown\) & **4** & 1.00 & 0.99 & 1.00 & 0.95 & 0.80 & 0.87 \\ \(\bigtriangledown\) & **5** & 0.99 & 1.00 & 0.99 & 0.72 & 0.94 & 0.82 \\ \(\bigtriangledown\) & **6** & 0.99 & 0.98 & 0.98 & 0.88 & 0.82 & 0.85 \\ \(\backsim\) & **7** & 0.97 & 0.98 & 0.97 & 0.88 & 0.85 & 0.87 \\ \(\backsim\) & **8** & 0.99 & 0.94 & 0.96 & 0.96 & 0.82 & 0.89 \\ \(\backsim\) & **9** & 0.98 & 0.98 & 0.98 & 0.84 & 0.67 & 0.75 \\ \(\backsim\) & **10** & 1.00 & 0.99 & 0.99 & 0.98 & 0.52 & 0.68 \\ \(\backsim\) & **11** & 1.00 & 1.00 & 1.00 & 0.91 & 0.95 & 0.93 \\ \(\backsim\) & **12** & 1.00 & 1.00 & 1.00 & 0.76 & 0.66 & 0.71 \\ \(\backsim\) & **13** & 0.99 & 1.00 & 0.99 & 0.71 & 0.88 & 0.79 \\ \(\backsim\) & **14** & 0.99 & 1.00 & 0.99 & 0.85 & 0.88 & 0.86 \\ \(\backsim\) & **15** & 1.00 & 1.00 & 1.00 & 0.90 & 0.89 & 0.89 \\ \(\backsim\) & **16** & 0.99 & 1.00 & 1.00 & 0.88 & 0.87 & 0.87 \\ \(\backsim\) & **17** & 0.97 & 0.99 & 0.98 & 0.81 & 0.76 & 0.79 \\ \(\backsim\) & **18** & 0.99 & 0.97 & 0.98 & 0.69 & 0.93 & 0.79 \\ \(\backsim\) & **19** & 0.98 & 0.99 & 0.98 & 0.88 & 0.87 & 0.87 \\ \(\backsim\) & **20** & 1.00 & 0.99 & 0.99 & 0.96 & 0.90 & 0.92 \\ \(\backsim\) & **21** & 1.00 & 0.96 & 0.98 & 0.76 & 0.77 & 0.77 \\ \(\backsim\) & **22** & 0.99 & 0.99 & 0.99 & 0.80 & 0.85 & 0.82 \\ \(\backsim\) & **23** & 1.00 & 0.99 & 0.99 & 0.76 & 0.90 & 0.83 \\ \(\backsim\) & **24** & 0.96 & 0.99 & 0.97 & 0.91 & 0.88 & 0.90 \\ \(\backsim\) & **25** & 1.00 & 0.99 & 1.00 & 0.76 & 0.90 & 0.83 \\ \(\backsim\) & **26** & 1.00 & 1.00 & 1.00 & 0.84 & 0.79 & 0.82 \\ \(\backsim\) & **27** & 1.00 & 0.98 & 0.99 & 0.62 & 0.81 & 0.71 \\ \(\backsim\) & **28** & 0.98 & 0.98 & 0.98 & 0.82 & 0.56 & 0.67 \\ \hline \end{tabular}
\end{table}
Table 3: The results and comparison of the CNN and VGG16 models for the 29 classes of the Hijia dataset.