# Dhad--A Children's Handwritten Arabic Characters Dataset for Automated Recognition
## 7 Results
### Experiment Two--Custom CNN Model  
In Experiment Two, a custom CNN model, drawing inspiration from the existing literature [4, 5, 13, 14, 15], was meticulously crafted and subsequently trained on both the Dhad and Hijja datasets. A detailed analysis of the model's training dynamics, as depicted in Figure 13, offers invaluable insights into its performance and adaptability. Figure 11: Loss curves for different dropout variations with MobileNet on Dhad dataset. Figure 12: Loss curves for different dropout variations and data augmentation with MobileNet on Dhad dataset. Upon scrutinizing the training accuracy plots, a discernible positive exponential trend is evident, aligning with typical training behaviour. However, an intriguing observation is the model's accelerated convergence, achieving desirable accuracy at a slightly quicker pace compared to other models. Nevertheless, the validation phase unraveled some concerns. While the validation accuracy initially mirrored the training trajectory, a conspicuous divergence emerged after the 20 th epoch, signaling the onset of overfitting. This overfitting propensity is further accentuated in the loss curves. After the 20 th epoch, a palpable uptick in validation loss becomes evident, corroborating the overfitting suspiciions. Such concerns are further compounded upon examining the convergence metrics; as detailed in Table 4, the model's loss values upon convergence are unexpectedly elevated for both the Dhad and Hijja datasets. Specifically, for the Dhad dataset, the model managed to attain a validation accuracy of 89% but manifested a relatively elevated validation loss of 0.3862. Conversely, the Hijja dataset witnessed a more pronounced performance disparity, with the model registering a diminished accuracy of 75% accompanied by a markedly higher validation loss of 0.8382. Table 5 provides a comprehensive overview of the custom model's test performance metrics on both the Dhad and Hijja datasets. A cursory examination of these results reveals a coherent alignment with the model's validation trajectory, reaffirming the standard train-validate-test paradigm, where the performances across validation and test phases remain largely congruent. For the Dhad dataset, the custom model demonstrated a commendable test accuracy of 88%, coupled with a test loss metric of 0.3988. Additionally, the model's \(F1\) score stood impressively at 0.89, underscoring its proficiency in maintaining a harmonious balance between precision and recall. Conversely, when evaluated on the Hijja dataset, the model's performance exhibited a discernible decline, registering a test accuracy of 74%. The associated test loss and \(F1\) score metrics further elucidate this observation, standing at 0.8693 and 0.75, respectively. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Training Loss** & **Training Accuracy** & **Validation Loss** & **Validation Accuracy** \\ \hline Dhad Dataset & 0.2554 & 0.9344 & 0.3862 & 0.8919 \\ Hijja Dataset & 0.5761 & 0.8354 & 0.8382 & 0.7515 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training performance of custom CNN model on Dhad and Hijja datasets. Figure 13: Training accuracy and loss curves for custom CNN model on Dhad and Hijja datasets. The nuanced performance trajectories across the two datasets are further corroborated by the AUC curves, meticulously depicted in Figure 14. The Dhad dataset witnessed a marginally superior performance, with the model achieving an AUC of 0.99, indicative of its robust discriminatory prowess. In stark contrast, the Hijja dataset, although exhibiting a commendable AUC value of 0.98, revealed a more scattered performance distribution across classes, emphasizing the inherent challenges and intricacies associated with character recognition tasks on this dataset. Figure 15 provides a comprehensive confusion matrix for both the Dhad and Hijja datasets.