# Dhad--A Children's Handwritten Arabic Characters Dataset for Automated Recognition
## 8 Discussion  
The exploration of handwritten Arabic character recognition, particularly among children, presents both challenges and opportunities. The results obtained from the experiments provided valuable insights into the effectiveness of deep learning models and traditional classifiers for this specific task. In this discussion, we delve deeper into the insights derived from the results, critically analysing them against the existing literature, evaluating the methodologies employed and highlighting potential avenues for future research.  
* Fine-tuning of Existing Models: An important insight from the performed experiments is the unparalleled efficacy of fine-tuning existing deep learning architectures. This approach resonates with the existing literature [29; 30; 31], highlighting the potential of harnessing pre-trained models fine-tuned for application-specific tasks. The flexibility of fine-tuning, which combines using general features with adjusting to specific dataset details, highlights its essential importance. Especially in situations with limited computing power, its ability to achieve impressive results quickly becomes clearly noticeable.
* Custom CNN Model: In comparison to the established deep architectures, our simpler custom CNN achieved good results in classification. However, the performance did not exceed the fine-tuned pre-trained models. These findings align with what is commonly discussed in current research [32; 33], emphasizing that simpler models can be easily affected by minor changes. This highlights the need for cautious interpretations and emphasizes the extra computing work needed when starting from scratch with new models.
* Two-stage Pipeline with Conventional Classifiers: Our exploration of a two-part process, combining deep visual feature extraction with traditional classification methods, resulted in less-than-ideal results. These results are consistent with existing research, highlighting the importance of end-to-end deep learning models trained effectively at once. The shortcomings arising from separate feature extraction and classification emphasize the need for unified model training, bringing together all elements to better achieve the main goal.
* Dataset Dynamics: At the heart of the model's performance variations lies the quality of the dataset. Our studies highlight the superiority of the Dhad dataset when compared to the Hijja one, likely due to clearer pixels and reduced interference. These insights highlight the crucial importance of careful dataset preparation, underscoring its fundamental role in shaping the best possible model results.
* Navigating Class Confounders: A recurring pattern throughout our experimental journey centres on the differentiation between certain class pairs, particularly \"t'aa-yaa\" and \"ayen-hamzah\". The blending of visual similarities among these classes leads to frequent misclassifications, highlighting the need for future efforts to develop more detailed training samples. Tackling this challenge requires a focused effort to enhance the dataset with diverse class examples, enhancing the model's ability to accurately distinguish categories.  
While the experiments offer valuable insights, they are not devoid of limitations. The use of a limited number of datasets, potential biases in data curation, and the absence of real-world noise simulations may limit the external validity of the findings. Furthermore, the focus on specific architectures and classifiers suggests a comprehensive exploration of the deep learning and traditional machine learning models. Potential future research directions can be as follows:  
1. Enhanced dataset curation, incorporating diverse writing styles, variations, and real-world noise simulations.
2. Comparative evaluations encompassing a broader spectrum of architectures, optimization techniques, and data augmentation strategies.
3. Exploration of ensemble methodologies, blending the strengths of multiple models to foster enhanced recognition capabilities.