## 2 Related Works  
In this section, we will discuss related research. Various sign language recognition techniques have been studied to improve the accuracy of sign language detection. Tables 1 and 2 summarize key related works in this area. [PERSON] et al. proposed the development of an automatic recognition system specifically designed for Indian Sign Language numerals (0-9). The system was built using a self-created database of 1000 images, with 100 images dedicated to each numeral. To effectively capture the distinguishing features of these numerals, the study employed advanced feature extraction techniques, including shape descriptors, scale-invariant feature transform (SIFT), and histogram of oriented gradients (HOG). For classification, two robust machine learning models, artificial neural networks (ANN) and support vector machine (SVM), were utilized. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors** & **Techniques and Their Explanations** & **Backbone Network** \\ \hline [PERSON] et al. [19] & The paper developed an Indian Sign Language numeral recognition system (0–9) & SVM \\ \hline [PERSON] et al. [20] & The paper employed two layers of image processing: the first for whole images, and the second for hand landmarks. & CNN \\ \hline [PERSON] et al. [21] & The paper presented a system for Indian Sign Language recognition using the SURF (Speeded Up Robust Features) method for feature extraction, combined with SVM (support vector machine) and CNN (convolutional neural network) classifiers. The system utilizes the Bag of Visual Words (BOVW) model to enhance the recognition process. & CNN \\ \hline [PERSON] et al. [22] & The paper employed LSTM and GRU models, combined them sequentially, used dropout for regularization, and trained on the IISL2020 dataset to enhance Indian Sign Language recognition. & LSTM and GRU \\ \hline [PERSON] et al. [23] & The paper used a transformer encoder with positional embedding patches and self-attention layers, followed by a multilayer perceptron network, to enhance the recognition of fixed Indian Sign Language. & Transformer \\ \hline [PERSON] et al. [24] & The paper employed transfer learning with various pretrained models (MobileNet, Xception, Inception, InceptionResNet, DenseNet, BiT) and vision transformers (ViT, Swin) to recognize Arabic Sign Language, comparing their performance with CNNs trained from scratch. & ViT, Swin \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of sign language recognition techniques and their backbone networks. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors** & **Techniques and Their Explanations** & **Backbone Network** \\ \hline [PERSON] et al. [25] & The paper developed a sign language recognition system employing video-based, skeleton-based, and deep learning techniques, enhanced by data augmentation for robust performance across various environments. & PoseConv3D \\ \hline [PERSON] et al. [26] & The paper proposed a neuroevolution approach to enhance fingerspelling recognition in sign language by optimizing deep neural networks through evolutionary algorithms. The technique involves processing ASL fingerspelling images into normalized keypoints, which are then used as inputs for neural networks. & Neuroevolution-optimized deep neural network \\ \hline [PERSON] et al. [17] & The paper proposed a method for detecting start and end points of sign language utterances using the MediaPipe & R(2 + 1)D \\  & Holistic model. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of keypoint-based techniques for sign language recognition. The proposed system achieved a remarkable accuracy of up to 99%, demonstrating its potential for accurately recognizing Indian Sign Language numerals [19]. In the field of computer vision, convolutional neural networks (CNNs) have become foundational architectures, significantly impacting various applications, including sign language recognition, facial recognition, object detection, medical image analysis, and autonomous driving. For instance, [PERSON] et al.