# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 2 Related Works
### Overview of SL Classification Research Based on Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **SL** & **Feature Extraction/Characterization** & **Accuracy** & **Ref.** \\ \hline \multirow{3}{*}{SVM} & Indian & HOG based on Taguchi and TOPSIS & 92\% for ISL and 99.2\% for ASL & [29] \\ \cline{2-5}  & American & Used images captured in & 99.80\% & [4] \\  & complex backgrounds & & & \\ \hline KNN & Arabic & Classified 28-letter Arabic alphabet using & 90.25\% & [3] \\  & & 9240 images. & & \\ \hline LDA & American & Used five classes with 50 input images. & 62\% & [31] \\ \hline MLP & American & Used static ASL gesture & 96.96\% & [28] \\ \hline \multirow{3}{*}{\begin{tabular}{c} American \\ British \\ \end{tabular} } & Italian & Used multiangle images, frontal & 99\% with VGG19, 97\% & [23] \\  & & images only. & with CNN & [23] \\ \hline \multirow{3}{*}{\begin{tabular}{c} American \\ British \\ \end{tabular} } & American & CNN based on AlexNet and VGG16 for & 99.82\% & [30] \\  & & hand gestures & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Turkish \\ \end{tabular} } & 3D CNN for 26 BSL & 98.00\% & [36] \\  & alphabets classification & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} CNN \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Indian \\ \end{tabular} } & Employed feature extraction module & Best baseline model achieved & [34] \\  & designed with parallel convolutions with & a 62.02\% accuracy. & [34] \\  & different dilation rates & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} CNN \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Indian \\ \end{tabular} } & Used a 2D CNN for feature extraction, & 72.3\% on signed sentences & \\  & where twelve 3D features at time t & and 89.5\% on isolated & [32] \\  & were processed & sign words. & \\ \cline{2-5}  & \multirow{3}{*}{\begin{tabular}{c} 3D CNN for videos for eight \\ emergency situations \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} MobileNet-LSTM 99.7\% for \\ signer dependent and 72.4\% \\ for signer-independent \\ \end{tabular} } & \multirow{3}{*}{[33]} \\  & & 3D CNN was used to recognize & 98\% for observed data and \\  & & 25 gestures & 85\% for new data & [7] \\ \cline{2-5}  & \multirow{3}{*}{
\begin{tabular}{c} Arabic \\ \end{tabular} } & 3D CNN skeleton network and a 2D point & In dependent mode: 98.39\% & \\  & convolution network using 80 static and & for dynamic signs that were repeated five & for dynamic, 88.89\% for \\  & & & static signs. In independent & [35] \\  & times by 40 signers & & mode: 96.69\% for dynamic, & 86.34\% for static. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of intelligent vision-based methods using various feature extraction techniques. In transfer learning, the insights, features, or representations learned from the source task are transferred and utilized to enhance the learning process or performance on the target task. The network's features of the pretrained model are reused, the output of the last layer is removed and replaced by a new layer for a different set of classes of the new task. Transfer learning was used for SL classification in ref. [14], wherein a YCbCr segmentation technique using skin color as a basis and the local binary pattern were utilized to accurately segment shapes and capture texture features and local shape information. The VGG-19 model was adjusted to obtain features, which were then combined with manually crafted features using a serial fusion technique. This approach achieved an accuracy of 98.44%. In ref. [2], a combination of three models including 3D CNN, mixed RNN-LSTM, and YOLO for advanced object recognition in a video dataset created for eight different emergency situations. The frames were extracted and used to evaluate these models, which attained 82% for 3D-CNN, 98% for RNN-LSTM, and 99.6% for YOLO models.