# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 2 Related Works
### Overview of SL Classification Research Based on Datasets  
Many studies in the literature have utilized public datasets, with a focus on American SL due to its accessibility. Other SLs often require researchers to create their own datasets, which they then publish for other researchers to use. In our review of the previous literature, we found that ArSL itself was diverse due to variations between cities and dialects. One significant challenge for SL research is the limited data availability, particularly for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **SL** & **Feature Extraction/Characterization** & **Accuracy** & **Ref.** \\ \hline \multirow{4}{*}{RNN} & \multirow{4}{*}{American} & Sphere radius, angles between fingers, & \multirow{4}{*}{99.44\%} & \multirow{4}{*}{[38]} \\  & & and distance between finger positions & & \\  & & using 2600 samples & & \\ \cline{2-4}  & Arabic & Recurrent neural networks (RNN) & 95.11\%. & [37] \\ \hline \multirow{4}{*}{LSTM} & \multirow{4}{*}{Indian} & 942 signed sentences using 35 different sign words & 72.3\% for signed sentences and 89.5\% for isolated sign words & \\  & & & & words \\ \cline{2-4}  & & Recurrent neural network with long & \multirow{2}{*}{98\%} & \multirow{2}{*}{[2]} \\  & & short-term memory (RNN-LSTM) & & \\ \hline \multirow{4}{*}{Transfer Learning} & \multirow{4}{*}{Arabic} & VGG-19 & 98.44\% & [14] \\  & & VGG-16 and ResNet152 with ArSL & \multirow{2}{*}{99\%} & [39; 40] \\  & & classification & & \\ \cline{1-1} \cline{2-4}  & \multirow{4}{*}{Indian} & VGG-16 and YOLO (You Only Look Once) & \multirow{4}{*}{99.60\%} & \multirow{2}{*}{[2]} \\ \cline{1-1}  & & v5, for advanced object & & \\ \cline{1-1}  & & detection’algorithm & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
ArSL [33]. Datasets, also called databases in earlier research on SL, can be obtained through public or private repositories [16; 23; 47; 59]. Datasets of SL can be divided into three main categories: finger spelling, isolated signs, and continuous signs. Finger spelling datasets are based primarily on the shape and direction of the finger. Most of the numbers and alphabets for SL are static and use only fingers. Isolated datasets are equivalent to spoken words and can be static (i.e., images) or dynamic (i.e., videos). Continuous SL datasets are formed from videos of signers representing words and sentences in the language. The evaluation of SL datasets should consider several factors including size, background conditions, and sign representation. One of the factors that determine the variability of an SL database is the number of signers, which is important for evaluating the generalization of recognition systems. Signer-independent recognition systems are evaluated on signers other than those involved in the system training by increasing the number of signers. The number of samples per sign is another factor whereby having several samples per sign with some variations per sample is important for training deep learning models. Samples in SL datasets can be grayscale or RGB format. Any researcher interested in this field can obtain an SL dataset from online repositories such as Kaggle or by building a dataset [3; 33; 34; 35; 51; 63; 64]. Table 3 summarizes various datasets in terms of isolated vs. continuous, static vs. dynamic, language, recognition model, lexicon size, acquisition mode, signs, signers, single vs. double handed, and background conditions. Existing datasets available for ArSL were explored from the point of view of isolated and continuous signing. To the best of our knowledge, there were only two datasets available for ArSL, where the second was developed from the first by [PERSON] et al. [12]. The ArSL alphabets dataset consisted of 54,049 images compiled by more than 40 volunteers for 32 signs and alphabets. More details are given in Section 4.