# Towards Accurate Children's Arabic Handwriting Recognition via Deep Learning
## 2 Related Work
### Deep Learning for Handwritten Character Recognition  
Recently, researchers have shown an increased interest in handwritten character recognition. In 2017, [PERSON] et al. [16] collected an extensive dataset of handwritten Arabic characters to train a deep learning model. Furthermore, they implemented optimization methods on a CNN. The proposed CNN model achieved 94.9% accuracy. In 2017, [PERSON] et al. [17] proposed a deep neural network (DNN) for handwritten Arabic character recognition that used CNN models. In addition, they used dropout and batch normalization as regularization techniques. They tested the approach with two datasets: the AHCD and the AIA9K. The experimental results showed that the CNN model achieved 97.6% accuracy for the AHCD and 94.8% accuracy for the AIA9K. As recognizing handwritten characters has become more critical, recognizing children's handwriting has emerged as a specific area of research. In 2021, [PERSON] et al. [1] explored this area by conducting a study using the Arabic language in recognition systems and creating a dataset called Hijja, which contains Arabic letters produced by children aged 7-12 years. Five hundred and ninety-one participants contributed 47,434 characters to their dataset. They also proposed a model for \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**References** & **Year** & **Model** & **Dataset** & **Type** & **Size** & **Accuracy** \\ \hline [PERSON] et al. [10] & 2010 & MLP & & & & 94.93\% \\ [PERSON] et al. [11] & 2017 & MLP & CMATERDB & Digits & 3000 & 93.8\% \\ [PERSON] et al. [12] & 2017 & RBM-CNN & & & & 98.59 \% \\ \hline [PERSON] et al. [13] & 2017 & DNN & ADBase & Digits & 70,000 & 99.57\% \\  & & & HACDB & Chars & 6600 & 97.32\% \\ \hline [PERSON] et al. [14] & 2018 & CNN & MADBase & & 70,000 & 99.32\% \\  & & & MNIST & & 70,000 & 99.32\% \\  & & & HODA & & 80,000 & 99.32\% \\  & & & Urdu & Digits & 8500 & 99.32\% \\  & & & DHCD & & 20,000 & 99.32\% \\  & & & Combined & & 20,000 & 99.26\% \\ \hline [PERSON] et al. [15] & 2019 & CNN & CMATERDB & Digits & 3000 & 99.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of deep learning-based handwritten digit recognition models. automatic handwriting recognition based on a CNN. The model consisted of three convolutional layers, three pooling layers, and four fully connected layers. In addition, the ReLU activation function was used after each convolutional layer along with 80% dropout in the fully connected layers to avoid overfitting. The Hijja and AHCD datasets were used to train this model. The proposed CNN model outperformed the other model presented in [16] on the Hijja and AHCD datasets, achieving 88% and 97% accuracy, respectively. In 2020, [PERSON] et al. [18] studied the effectiveness of the ResNet-18 architecture in recognizing handwritten Arabic letters. They employed a fully connected layer and a dropout layer in the original architecture. They proposed two ensemble models: the first one was based on the original ResNet-18 and modified ResNet-18 with one fully connected layer with/without a dropout layer, and the second one was based on the original ResNet-18 and modified ResNet-18 with two fully connected layers added to both the original and the modified ResNet-18 with/without a dropout layer. The AHCD dataset was used to train and evaluate the proposed algorithm, with the original ResNet-18 achieving the best result of 98.30%. The ensemble model with one fully connected layer and an ensemble model with two fully connected layers paired with a dropout layer achieved an accuracy of 98.00% and 98.03%, respectively. In 2020, [PERSON] et al. [19] described a new categorization strategy based on a CNN relative neighborhood graph (RNG) with Gabriel's graph (GG). The method was evaluated using recognition of isolated handwritten Arabic characters. The method involved directing the CNN through a filtering layer, thereby narrowing the set of probable classes for a query item. For this purpose, the rules of the RNG and GG were integrated.