# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 2 Related Works
### Overview of SL Classification Research Based on Arabic Language  
ArSL classification deals with the identification of alphabets (SLA) and ideographic (ISL), whereby the latter is based on the diversity of Arabic dialects in countries and their cultures. In 2001, the Arab Federation of the Deaf designated ArSL as the official language for individuals with speech and hearing impairments in Arab countries. Despite Arabic being widely spoken, ArSL is still undergoing development. One major challenge faced by ArSL is \"Diglossia,\" where regional dialects are spoken instead of the written language in each nation. In this regard, different spoken dialects made different ArSLs [18], but still, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sign Language** & **Acronym** & **No of studies** & **Studies [2020–2023]** \\ \hline Arabic & ArSL & 7 & [3; 7; 10; 11; 39; 40; 48] \\ \hline Algerian & AISL & 1 & [50] \\ \hline American & ASL & 11 & [16; 22; 38; 41; 42; 43; 44; 45; 61; 62] \\ \hline Bangla & BdSL & 1 & [55] \\ \hline British & BSL & 1 & [36] \\ \hline Chinese & CSL & 2 & [26; 28; 56; 57] \\ \hline Indian & ISL & 2 & [52; 53; 54] \\ \hline Indonesian & ISINDO & 1 & [58] \\ \hline Italian & LIS & 1 & [23] \\ \hline Mexican & MSL & 1 & [47] \\ \hline Pakistani & PKSL & 1 & [51] \\ \hline Peruvian & PSL & 1 & [59] \\ \hline Turkish & TSL & 1 & [28] \\ \hline Urdu & USL & 1 & [24] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of SL research reviewed in this work from 2020 to 2023, listed alphabetically according to their acronyms. few studies have addressed this problem. The focus of existing works has been given to alphabetic ArSL classification [3; 10; 12]. The datasets used in ArSL research works were mostly collected by the researchers themselves. In ref. [7], a feature extractor with deep behavior was used to deal with the minor details of ArSL. A 3D CNN was used to recognize 25 gestures from the ArSL dictionary. The system achieved a 98% accuracy for observed data and an 85% average accuracy for new data. The results could be improved as more data from more different signers were included. In ref. [11], a vision-based system by applying CNN for recognizing ArSL letters and translating them into Arabic speech was proposed. The proposed system automatically detected hand sign letters and spoke out the result in Arabic with a deep learning model which achieved a 90% accuracy. Their results assured that using deep learning such as CNN was highly dependable and encouraging. In ref. [35], a 3D CNN skeleton network and 2D CNN models were used for ArSL classification using 80 static and dynamic signs. The dataset was created by repeating each sign five times by 40 signers. Their models attained accuracies of 98.39% for dynamic, 88.89% for static signs in the dependent mode, and 96.69% for dynamic, 86.34% for static in the independent mode: When mixing both dependent and independent modes, the results achieved were 89.62% for signer dependent and 88.09% for signer independent. Additional recent research works presented a model utilizing fine-tuning with deep learning for the specific task of recognizing ArSL [39; 40], which initiated the research direction of using transfer learning for ArSL and also created the datasets used in this study. CNN models reducing the size of the dataset required for training and at the same time, reaching a higher accuracy, were investigated [40]. Models like VGG-16 and ResNet152 were adjusted using the ArSL dataset. To address the class size imbalance, random undersampling was utilized, reducing the image count from 54,049 to 25,600. Moreover, the study in ref. [39] presented a modified ResNet18 model and reported the best accuracy score compared to the previous models, which was 1.87% higher than the existing works using ResNet. A recent study [49] explored continuous ArSL classification using video data and CNN-LSTM-SelfMLP models with MobileNetV2 and ResNet18 backbones for feature extraction. Their study achieved an 87.7% average accuracy.