# Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures
## 2 Related Works and Motivation
[39] established a CNN model to detect printed Arabic letters and numbers. Using an AHCD dataset, the model was trained. This study used data-augmentation techniques to improve model performance and detection outcomes. The experiment demonstrated that the proposed strategy might achieve a success rate of 97.2 percent. The model's accuracy increased to 97.7 percent once data augmentation was implemented. [PERSON] et al. [40] created a method for segmenting Arabic words for recognition purposes using a convolutional neural network (CNN) and mathematical morphology operations (MMOs). In their study, the authors offered a directed CNN and achieved better performance than a standard CNN. [PERSON] et al. [41] presented a deep-belief neural network (DBNN) for identifying handwritten Arabic characters/words. The proposed model began with the row data before proceeding to the unsupervised learning technique. This model's performance on the HACDB dataset was 97.9 percent accurate. [PERSON] et al. [42] developed a fuzzy classifier with structural properties for Arabic-handwritten-word-recognition offline systems based on segmentation procedures. Before extracting features using invariant pseudo-Zernike moments, the model splits characters into five distinct categories. According to the study's findings, the proposed model achieved a high level of accuracy for the IFN/ENIT database of 93.8%. Based on the generic-feature-independent-pyramid multilevel model (GFIPML), [PERSON] et al. [43] developed a method for recognizing Arabic handwriting. To evaluate their system's performance, the authors utilized the AHDB dataset and obtained better outcomes. They combined local phase quantization (LPQ) with multiple binarized statistical image features (BSIfs) to enhance the recognition. The proposed system achieved an accuracy of 98.39%. However, in a previous study, [PERSON] et al. [44] compared the performance of multiple CNN networks to statistical descriptors derived from the PML model. The highest recognition rate attained by the LPQ descriptor was 91.52%. Another common application of handwriting recognition is Arabic handwritten literal amount recognition. [PERSON] et al. [45] performed numerous experiments with convolutional neural networks (CNNs), such as basic CNN, VGG-16, and ResNet, which were developed using regularization approaches, such as dropout and data augmentation. The results demonstrated that CNN architectures are more effective than previous approaches based on handmade characteristics. Since pre-trained models are trained on a general dataset and may not be suitable for Arabic handwriting classification, it is evident from the articles mentioned above that models created from scratch tend to produce better results. Models that have been pre-trained include machine-learning and deep-learning models that have been created and trained on a wide range of data. For example, the ImageNet dataset is used to train models and comprises thousands of images; however, models constructed utilizing the image dataset from the beginning of their training make them far more fit for the classification task due to their comprehensive understanding of the dataset. Later, adjusting the hidden layers of the deep-learning models makes them more suitable for the classification challenge. In this work, we tried to overcome this limitation by building the model specifically for Arabic handwritten character classification. Table 1 summarizes the CNN models developed in the literature for Arabic handwriting recognition. Table 2 presents a summary of the Arabic handwriting datasets.