# Dhad--A Children's Handwritten Arabic Characters Dataset for Automated Recognition
## 7 Results
### Experiment Three-Classification of Deep Visual Features  
In Experiment Three, a sophisticated two-stage approach was devised to optimize the classification process, blending the strengths of deep learning feature extraction with the precision of traditional classifiers. The foundational component of this pipeline was the MobileNet architecture, renowned for its prowess in extracting intricate features from complex datasets. By utilizing MobileNet's capabilities, the experiment aimed to transform the raw data into a more discernible and compact representation, thereby facilitating more effective subsequent classification. In this context, we have used the MobileNet model pre-trained over the ImageNet and MobileNet models trained in Experiment One. Following the feature extraction phase, the extracted features were then subjected to three distinct conventional classifiers: SVM, RF, and MLP. SVM, a discriminative classifier, operates by finding the optimal hyperplane that best separates the data into distinct classes, making it particularly adept at handling high-dimensional feature spaces. Conversely, RF, an ensemble learning method, constructs multiple decision trees during training and outputs the class that is the mode of the classes of individual trees for classification tasks, thereby leveraging the wisdom of multiple trees to enhance accuracy and robustness. On the other hand, MLP is known for its fully connected neural architecture to extract the hidden patterns from the input feature vector. Figure 14: Confusion matrix for custom CNN model on Dhad and Hijia datasets. (**a**) Dhad and (**b**) Hijia. Figure 15: AUC curves of custom CNN model for Dhad and Hijia datasets. (**a**) Dhad and (**b**) Hijia. Delving into the results encapsulated in Table 6, a discernible pattern emerges. For the Dhad dataset, the MobileNet + SVM ensemble manifested as the optimal configuration, demonstrating its prowess with a validation accuracy of 89%, which was corroborated by the test accuracy standing at a commendable 88%. Further fortifying its performance credentials, the ensemble yielded an \(F1\) score of 0.88, underscoring its balanced precision and recall capabilities. Similarly, when transposed to the Hijja dataset, the MobileNet+SVM configuration continued its dominance, albeit with slightly diminished metrics. A validation accuracy of 73% and a corresponding test accuracy of 72% were achieved, along with an \(F1\) score of 0.73, signifying a robust performance despite the dataset's inherent complexities. In context to the use of ImageNet pre-trained and Experiment One trained model, it can be observed that the ImageNet pre-trained model resulted in better performance. Figures 16-21 provide a detailed representation of the confusion matrices derived from the SVM, RF, and MLP classifiers when employed with deep visual features from the ImageNet pre-trained and Experiment One trained MobileNet model on both the Dhad and Hijja datasets. Complementing these visual representations, the findings elucidated in the table corroborate the classifiers' performance metrics. Notably, SVM emerges as the superior performer across all the cases. However, when contextualized within the datasets, a nuanced observation surfaces. The Dhad dataset consistently showcases enhanced performance metrics in comparison to its Hijja counterpart. This disparity in performance underscores the Dhad dataset's superior quality, likely attributed to meticulous data curation, reduced noise levels, or other preprocessing enhancements. Such insights are pivotal, as they not only validate the efficacy of the classification pipeline but also emphasize the pivotal role of dataset quality in influencing model performance and outcomes.