# Dhad--A Children's Handwritten Arabic Characters Dataset for Automated Recognition
## 3 Related Work
### Deep Learning-Based Classification of Children's Arabic Handwriting  
A summary of the latest research related to children's handwritten Arabic character classification is presented in this section to demonstrate the state of the art. This section is organized in chronological order to better understand the developments over the years. [PERSON] et al. [12] in 2020 implemented a custom CNN model for the classification of Arabic characters using the AHCR, AHCD, and Hijja datasets. The authors reported an accuracy of 92.5% for the Hijja dataset. [PERSON] and [PERSON] [4] in 2021 introduced the Hijja dataset, which stands out as a unique collection focusing exclusively on letters written by children. This dataset, comprising 47,434 characters from 591 participants aged 7-12, filled a notable gap in existing resources, particularly for understanding the nuances of children's handwriting. A CNN model was developed and trained on the Hijja dataset, which achieved a test accuracy of 88%. However, the work lacked a detailed investigation on existing powerful models and ignored the practical implications of the research. Figure 1: The architecture of our custom CNN model. [PERSON] et al. [13] in 2022 introduced an OLReLU combined with a CNN architecture and a batch normalization layer to enhance performance in scenarios with imbalanced positive and negative vectors. Four datasets, including the AHCD, self-collected data, Modified National Institute of Standards and Technology (MNIST), and AlexU Isolated Alphabet (AIA9K), were used. The proposed model was able to achieve 90% accuracy for the Hijja dataset. [PERSON] and [PERSON] [5] recently in 2023 investigated the problem in more detail using custom developed CNN models and hybrid approaches. The authors made use of datasets from both adults (i.e., AHCD) and children (i.e., Hijja) to explore the performance, with a particular emphasis on the impact of different training datasets. The authors further investigated the problem of classifying by deploying a conventional machine learning pipeline of extracting visual features and classifying using classical machine learning models (e.g., SVM, KNN, and RF). The findings reveal that training the model on a combination of children's and adult datasets yields the best performance, achieving an impressive average accuracy of 92.78% in recognizing children's handwritten characters. Moreover, authors extended their investigation to the classification of writers into two groups (i.e., children and adults) using the proposed CNN model. The initial results showed an average accuracy of 89.28%, indicating the presence of confusable similarities in writing styles between adults and children. To enhance discrimination performance, the study suggested supplementary features based on Histogram of Oriented Gradients (HOGs) and statistical measures, which, when combined with CNN features, result in a significantly improved accuracy of 92.29%. [PERSON] et al. [14] in 2023 implemented a custom CNN architecture tailored for recognizing children's Arabic handwritten characters. The authors made use of the AHCD and Hijja datasets to train the model and achieved accuracies of 97% and 91% for AHCD and Hijja, respectively. Additionally, the authors introduced an innovative approach using character strokes as a filter to further enhance recognition accuracy. This method, combined with CNNs, demonstrated effectiveness in improving performance. The research compared the proposed model with the pre-trained EfficientNetV0 and reported better performance for the custom model. Moreover, a multi-model approach, integrating information about the number of strokes in a character, achieved an average prediction accuracy of 96% when Hijja was merged with AHCD. [PERSON] et al. [15] in 2023 implemented two deep learning-based models (i.e., custom CNN and pre-trained VGG16) for children's handwriting character recognition using Hijja and AHCD.