# Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures
## 2 Related Works and Motivation  
Handwriting recognition using CNNs has received attention in terms of research work in different languages, such as English [17; 18; 19; 20; 21], Arabic [1; 3; 7; 8; 9; 10; 22], Bangla [2], and Chinese [23; 24; 25; 26; 27; 28]. Recently, the focus on Arabic handwriting recognition has increased [29]. Researchers have developed different techniques to enhance recognition outcomes. According to [PERSON] et al. [3], CNN techniques outperform other feature-extraction and classification methods, especially with big datasets. This is not applicable for most of the studies on Arabic language recognition. Therefore, the authors created the Arabic Handwritten Characters Dataset (AHCD) and proposed a CNN model that achieved an accuracy of 94.9. Similarly, [PERSON] et al. [1] released Arabic handwritten alphabets in what is called \"the Hijja\" dataset. The dataset consists of samples written by children aged 7 to 12 years old. The researchers conducted handwriting-recognition experiments using a CNN trained on two datasets, the Hijja dataset and the Arabic Handwritten Character Dataset (AHCD). The proposed model achieved accuracies of 97% on the AHCD dataset and 88% on the Hijja dataset. These results indicate that they have outperformed the achieved results of [PERSON] et al. [3]. Using a different approach, [PERSON] and [PERSON] [13] merged an ML model with a deep-learning model to create a hybrid, taking advantage of CNN models in feature extraction and ML models in classification. The study achieved an accuracy of 96.3, proving the hybrid model's effectiveness. The result was better than those obtained in the original experiment performed on the same dataset (the Hijja) by [PERSON] and [PERSON] [1]. A CNN architecture known as Alexnet was utilized by [PERSON] et al. [7], which includes three layers of max pooling followed by three layers of fully connected convolutions. They investigated the impact of preprocessing on model improvement. The Alexnet model was trained and evaluated using two datasets, OIHACDB-40 and AHCD, and three learning strategies: training the CNN model from scratch, utilizing a transfer-learning technique, and fine-tuning the weights of the CNN architecture. The experimental outcomes demonstrated that the first technique outperformed the others, with 100 percent and 99.98 percent accuracy for the OIHACDB-40 and AHCD datasets, respectively. [PERSON] et al. [30] established a vast, complicated dataset of Arabic handwritten characters (HMBD). They implemented a deep-learning (DL) system with two convolutional-neural-network (CNN) architectures (called HMB1 and HMB2), using optimization, regularization, and dropout techniques. They employed [PERSON] et al. [3] as a controlled study throughout their 16 experiments with the HMBD, CMATER, and AIA9k datasets. The study suggested that data augmentation helped increase testing accuracy and reduce overfitting. Data augmentation increased the volume of input data; as a result, the architectures were learned and trained using more data. The top results for HMBD, CMATER, and AIA9k were 90.7%, 97.3%, and 98.4%, respectively. [PERSON] [31] and [PERSON] et al. [32] built CNN models that were trained and tested on AHCD to improve AHCD performance. Three convolutional layers and one fully connected layer comprised the model of [30]. In addition, two regularization methods, dropout and batch normalization, with distinct data-augmentation techniques, were employed to enhance the model's performance. Using the AIA9k and AHCD datasets to train and test the model, the accuracies were 94.8 and 97.6 percent, respectively. Similarly, the CNN design suggested in [31] comprised four convolutional layers, two max-pooling layers, and three fully connected layers. The authors examined various epochs and batch sizes and found that 40 epochs with a batch size of 16 produced the best results for training of the model. Based on empirical findings, the model accuracy achieved was 97.2%.