# Dhad--A Children's Handwritten Arabic Characters Dataset for Automated Recognition
## 7 Results
### Experiment One--Pre-Trained CNN Models  
In Experiment One, where ResNet50, MobileNet, and DenseNet121 underwent fine-tuning on the Dhad and Hijja datasets, several intriguing training dynamics were observed (see Figures 4 and 5). The training accuracy curves consistently exhibited a positive exponential trajectory, reflecting the models' progressive refinement and learning. Concurrently, the training loss curves showcased a negative exponential pattern, suggesting a consistent reduction in training errors--both patterns emblematic of typical training behaviour. While a nuanced performance advantage was discerned in favor of ResNet50 from the training curves, this superiority was marginal. However, the validation phase painted a slightly different picture. Although the validation curves initially mirrored the training trajectories, a noticeable degradation in performance became evident after a certain epoch. This divergence, particularly conspicuous in the validation loss curves post-epoch 7, is a clear manifestation of overfitting--a phenomenon exacerbated by the datasets' inherent simplicity. Such insights underscore the importance of leveraging validation metrics as they provide a clearer lens into the model's generalization progress. Intriguingly, when evaluating based on validation performance, DenseNet121 emerged marginally superior, although the performance disparities among the three models remained modest, positioning them comparably in terms of efficacy on these datasets. A nuanced perspective on the models' training outcomes becomes apparent in the comparative analysis derived from Table 2. MobileNet emerged as the frontrunner for the Dhad dataset, boasting a validation loss of 0.2278 and a commendable accuracy of 0.9396. Conversely, for the Hijja dataset, DenseNet121 showcased its prowess with metrics of 0.4359 for validation loss and an accuracy score of 0.8920. Delving deeper into the model performances, both MobileNet and DenseNet121 exhibited closely matched capabilities, with only marginal differences in their efficacy. In stark contrast, ResNet50's performance trajectory leaned more towards pruning, hinting at potential redundancy or inefficiencies in its architecture. This behaviour can be attributed to ResNet50's heavier design, which might have rendered it more susceptible to overfitting, especially given the datasets' inherent simplicity. In contrast, MobileNet's leaner architecture seemingly conferred upon it a more adaptive and resilient nature, enabling it to outperform its counterparts. A comparative examination between the Dhad and Hijja datasets further illuminates this discussion. Predominantly, the pre-trained models showcased superior performance metrics on the Dhad dataset, underscoring its superior quality and efficacy in facilitating model training. Such observations align with the hypothesis positing Dhad's utilization of enhanced preprocessing methodologies, likely resulting in a cleaner, noise-attenuated dataset conducive for effective model learning. This superior data quality inherently empowered the models, enabling them to achieve heightened accuracies and reduced losses on the Dhad dataset compared to its Hijja counterpart. The testing phase (see Table 3) further substantiated the models' capabilities, revealing outcomes that closely mirrored their validation performance. Such consistency underscores the models' adeptness at capturing generalized features, enabling them to maintain consistent performance across previously unseen datasets. Specifically, for the Dhad dataset, MobileNet continued to demonstrate its efficacy, registering a test accuracy of 0.9359, a test loss of 0.2468, and an impressive \(F1\) score of 0.94. On the other hand, for the Hijja dataset, DenseNet121 emerged as the optimal performer, achieving a test accuracy of 0.8883, a test loss of 0.4919, and an \(F1\)-score of 0.89. Figure 4: Training accuracy and loss curves for pre-trained models on Dhad dataset.