# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 3 Research Methodology
### Classification  
The categorizing method begins after all statistics for the entire data set have been created. It is vital to split the dataset for training and testing before going on to classification. As a result, the entire dataset was split into 70:30 classes, with 960 photos used for training as well as 240 pictures in use for testing. After the training set is available, the following step is to input it into the machine learning model. The data were trained and forecasted using SVM, CNN, k-NN, logistic regression, and naive Bayes. Despite being considered a similar model, each has unique strengths and limitations, as SVM can effectively handle high-dimensional data and complex classification problems.  
Additionally, SVM has a solid theoretical foundation and is less prone to overfitting than other algorithms. However, one potential limitation of SVM is that it can be computationally expensive; SVM also relies heavily on the choice of the kernel function, and selecting an inappropriate kernel can lead to poor performance. During our research, we observed that linear kernels showed the best results. Naive Bayes is known for its simplicity and speed, making it a popular choice for text classification and other simple classification tasks. However, it strongly assumes that all features are independent, which can limit its accuracy for more complex problems. Logistic regression is relatively simple and fast but can handle more complex relationships between features than naive Bayes. However, it may struggle with high-dimensional datasets or non-linear relationships between features. k-NN is non-parametric, meaning it does not make any assumptions about the underlying distribution of the data. This can make it more flexible and accurate than some parametric methods but can also make it computationally expensive for large datasets. CNN is a powerful tool for image recognition tasks and has achieved state-of-the-art performance on various datasets. However, it is also complex and computationally expensive and can be difficult to train effectively without large amounts of data.  
It also became extremely important to understand the importance of explainable AI, which is crucial in decision making [32]. Real-time recognition was also created, allowing users to predict real-time motions while watching a video feed.  
Figure 8: Combination of steps used for extracting features and ultimately converting it to a visual word vector [31].  
In Figure 9, the x-axis represents the predicted label, which denotes the class or category our model assigned to that instance. In contrast, the y-axis represents the true label, which refers to the actual category of an instance in the dataset. The count or frequency of instances falling into each combination of true and expected labels is contained in the cells of the confusion matrix. In our example, the letters A-Z and the numerals 1-9 will create a 36 \(\times\) 36-dimension confusion matrix. Each cell will store the number of times the true label is either a number or a letter and the predicted label is a different number or letter. The confusion matrix column with the value 240 indicates 240 cases in which both the true and predicted labels correspond to the given combination.