# Towards Accurate Children's Arabic Handwriting Recognition via Deep Learning
## 2 Related Work
### Deep Learning for Handwritten Character Recognition
Experiments on the IFHCDB database showed that the proposed technique performed better than a standard CNN by 97.40%. In 2020, [PERSON] et al. [20] proposed a hybrid model based on the development of deep convolution neural networks (DCNN) followed by an SVM to recognize and classify the missing features that the DCNN did not correctly classify. They employed the k-means clustering approach to solving the multi-stroke Arabic character recognition problem, which refers to the difficulty of recognizing letters that have the same stroke but different numbers or positions of the dots. To train and assess the model, they employed AHCD [16] and 840 additional images. In terms of accuracy, the model outperformed [PERSON] et al's. [16] model, reaching an accuracy of 95.07%. In 2021, [PERSON] et al. [5] proposed a CNN for the recognition of handwritten Arabic characters, training the model on 16,800 images of handwritten Arabic characters. The proposed model was composed of six convolution layers with different functions to predict and recognize images and three fully connected layers to perform character recognition. The CNN model was applied to two batches: 40 and 256, achieving a 97.2% accuracy. After applying data augmentation, the model achieved an accuracy of 97.7%. In 2021, [PERSON] et al. [2] proposed a hybrid model combining an ML model and a deep learning model. They preprocessed the Hijja dataset introduced by [PERSON] et al. [1] and developed a CNN for feature extraction of the Arabic character images. They then passed it on to a hybrid model using an SVM and eXtreme Gradient Boosting classifiers. The CNN was trained with backpropagation. The CNNs, trained first with the softmax fully connected layer, were then employed as a feature extractor to train and evaluate the ML classifier. They conducted two experiments and attained a recognition rate of 96.3%. In 2021, [PERSON] et al. [21] investigated the effects of architectural complexity on the recognition of handwritten Arabic characters. The researchers introduced and used a dataset of handwritten Arabic characters (HMDB) and two CNN-based architectures: HMB1 and HMB2. The HMBD, CMATER, and AIA9k datasets were used to train and evaluate the two proposed architectures. The authors performed 16 experiments to investigate the effects of the weight initializers, optimizers, data augmentation, and regularization on the model accuracy. For the HMBD, CMATER, and AIA9k, the best accuracies obtained were 98.4%, 100%, and 99.0%, respectively. In 2021, [PERSON] et al. [22] addressed the recognition and text segmentation phases. They presented several solutions for text segmentation. A CNN was used in the recognition phase, and they proposed 14 different native CNN architectures. The model was trained and evaluated using the HMBD database, which includes 54,115 handwritten Arabic characters. Experiments were conducted using native CNN architectures and the highest accuracy was 91.96%. In the same study, a transfer learning (TL) and genetic algorithm (GA) approach called HMB-AHCR-DLGA was also developed to improve the recognition phase's training parameters and hyperparameters. For the latter technique, pre-trained CNN models (VGG16, VGG19, and MobileNetV2) were used. The best combinations were presented after five optimization experiments. The highest testing accuracy reported was 92.88%, achieved in the VGG16 experiment using the AdaMax weights optimizer. Table 2 summarizes these deep learning-based handwritten character recognition studies. Previous research has shown that CNNs have achieved high accuracy in recognizing numbers [14,15] and Arabic characters [1,5,18,21]. In addition, the focus has been exclusively on adult handwriting, which was used to train the Arabic handwriting recognition models. We found that the new Hijja dataset for children has not been comprehensively studied, with an accuracy of only 88% being achieved in [1]. On the other hand, in [2], the dataset was modified, and only the individual letters, i.e., 29 classes, were considered.