# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 3 Methodology
### General Framework
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & \
\begin{tabular}{c} **Background** \\ **Conditions** \\ \end{tabular} & **Hands** & **Signers** & **Signs** &
\begin{tabular}{c} **Acquisition** \\ **Mode** \\ \end{tabular} & **Lexicon Size** & **Method** & **Dataset** \\ \hline
[63] & \begin{tabular}{c} Nonuniform \\ background \\ \end{tabular} & \begin{tabular}{c} Single + \\ double \\ \end{tabular} & 4 & \begin{tabular}{c} 20 \\ meaningful \\ words of \\ Arabic signs \\ \end{tabular} & \begin{tabular}{c} Microsoft \\ Kinect v2 \\ camera \\ \end{tabular} & \
\begin{tabular}{c} 7350 RGB \\ videos, 7350 \\ depth videos \\ \end{tabular} &
\begin{tabular}{c} single \\ deep networks \\ \end{tabular} & ArSL \\ \hline
[49] & \begin{tabular}{c} Uniform \\ background \\ \end{tabular} & Both & 4 & \begin{tabular}{c} Manual, \\ nonmanual \\ gestures for \\ signs \\ \end{tabular} & \begin{tabular}{c} Kinect V2 \\ acquisition \\ system \\ \end{tabular} & \
\begin{tabular}{c} 667 videos \\ \end{tabular} &
\begin{tabular}{c} MobileNetV2, \\ ResNet18, \\ CNN- \\ LSTM- \\ \end{tabular} & ArSL \\ \hline
[33] & \begin{tabular}{c} Nonuniform \\ background \\ \end{tabular} & \begin{tabular}{c} Single + \\ double \\ \end{tabular} & 4 & 50 signs & \
\begin{tabular}{c} Kinect V2 \\ sensors \\ \end{tabular} &
\begin{tabular}{c} 6748 video \\ samples \\ \end{tabular} & CNN & CSL \\ \hline
[64] & \begin{tabular}{c} Nonuniform \\ background \\ \end{tabular} & \begin{tabular}{c} Single + \\ double \\ \end{tabular} & 50 & \
\begin{tabular}{c} 10,000 \\ utterances \\ each with \\ 10-30 \\ characters \\ \end{tabular} & Microsoft &
\begin{tabular}{c} 50,000 \\ Kinect 2.0 \\ \end{tabular} & CNN & CSL \\ \hline
[50] & \begin{tabular}{c} 22 \\ background \\ simple to \\ cluttered \\ \end{tabular} & 41 & \begin{tabular}{c} 65 sign \\ classes \\ \end{tabular} & \
\begin{tabular}{c} 6238 videos \\ \end{tabular} &
\begin{tabular}{c} SVM, \\ VGG16- \\ GRU, \\ I3D, BD- \\ GRU- \\ Attention \\ \end{tabular} & AISL \\ \hline
[51] & \begin{tabular}{c} Uniform \\ background \\ \end{tabular} & 10 & \begin{tabular}{c} Single- \\ alphabet \\ signals + 3 \\ dynamic \\ signs \\ \end{tabular} & 48 MP smart & 5120 images, \
\begin{tabular}{c} 5102 images, \\ 353 videos \\ \end{tabular} &
\begin{tabular}{c} Bag of \\ words + \\ SVM \\ \end{tabular} & PKSL \\ \hline
[65] & \begin{tabular}{c} Uniform \\ background \\ \end{tabular} & 40 & 80 signs & \
\begin{tabular}{c} Kinect V1, V2, \\ Sony cam \\ \end{tabular} & 16,000 \(\times\) 3 &
\begin{tabular}{c} Concatenation \\ of 3D \\ and 2D \\ CNN \\ \end{tabular} & ArSL \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cont. dense layer and one classification layer. On the other hand, three CNN architectures with some variants were proposed with a set of layers which include convolutions, pooling, and other fully connected layers, in order to produce an output of one of a number of possible classes. We proposed three different CNN architectures as can be seen in the figure and trained them from scratch on the dataset. The first two CNNs differed in the number of convolution layers, and the third CNN used image augmentation, batch normalization, and dropout techniques to train the models.