## 2 Related Works
### Image Processing and Gesture Classification Methods  
Image processing and gesture classification are crucial components of sign language recognition systems. These tasks can be divided into two primary parts: hand recognition and gesture classification. Various machine-learning techniques have been applied to each part, offering specific advantages in terms of accuracy, processing speed, and computational complexity.  
Hand recognition involves detecting and tracking the hand in an image or video stream. Convolutional Neural Networks (CNNs) are widely used for this purpose due to their ability to extract meaningful features from images and videos. CNNs are particularly robust against variations in hand position, scale, and rotation as shown in various works, such as [PERSON] et al. [27] and [PERSON] et al. [28]. More recently, with the already mentioned widespread of hand sign recognition techniques to embrace different languages, [PERSON] et al. [29] developed a CNN-based methodology for the Kazakh sign language, obtaining 95.7% of recognition rate.  
A recent application of this technology can be found in Google's MediaPipe library, used also in the work of [PERSON] et al. [25] for gesture recognition. MediaPipe is Google's real-time hand landmark detection in which a hybrid of six models was combined in tracking and predicting a sequence of 21 3D points used in detecting/inferring hand gestures. This further facilitates sensitive and accurate tracking in this approach without the need for costly add-on hardware and, hence, it is practical for general use [30].  
Once the hand is recognized, the next step is gesture classification, where the detected hand movements are interpreted as specific gestures. Several machine learning models have been used for gesture classification, including Random Forests, Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs).  
ANNs are powerful tools for gesture classification due to their ability to learn through data training. They can model complex non-linear decision boundaries, improving classification accuracy in real-time scenarios. [PERSON] et al. proposed using ANN for the classification of sign language, demonstrating its effectiveness in capturing the intricacies of sign gestures [27]. [PERSON] et al. also reported the great results of an ANN for gesture classification [31]. Although these are remarkable results, ANNs can sometimes be prone to overfitting, which can affect their performance.  
Support Vector Machines (SVMs) are another popular machine learning tool for supervised learning, capable of handling both classification and regression tasks. This methodology classifies data based on probabilities, making them suitable for binary and multi-class classification problems. SVMs can perform both linear and non-linear classification by using different kernel functions. [PERSON] et al. [27] used an SVM classifier to categorize signs into seven classes of the Pakistan Urdu language, showcasing its utility in sign language recognition. [PERSON] and [PERSON] [30] used SVM for classification, reporting that this system outperformed other machine learning algorithms.  
Random Forest models are robust for gesture classification due to their ability to handle non-linear data and aggregate multiple decision trees to improve overall accuracy. These models are effective in educational and gaming contexts, identifying complex gestures in variable environments. For instance, in the study of [PERSON] et al. [32], a hybrid model combining VGG16 for hand recognition and Random Forest for gesture classification showed significant improvement in gesture classification accuracy, because the ensemble nature of Random Forest reduces variance and overfitting, enhancing robustness and generalization.