# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 3 Research Methodology
### Experimental Setup  
This study is being carried out on a Python platform using Python 3.9. The SVM, CNN, logistic regression, naive Bayes, and k-NN algorithms are applied and put into practice to evaluate the mode's efficacy. The integrated development environment VS code was used throughout the entire procedure (IDE). We combined all these datasets into one that was pre-labeled, and it contained 42,000 photos that were considered from various research articles. Factors such as clarity, resemblance, the background used in the image, and file size were considered while evaluating image datasets. The device on which the study was run had a 2.2 GHz processor, 201 GB of storage on the disc, and 8 GB of RAM. Our research began when these specifications were set. The study used various research articles and books to be conducted, along with technical expertise and theoretical knowledge of image processing. Different parameters for picture conversion, such as RGB to HSV, were also an important element of the study.  
The system overview is detailed in Figure 2, which is divided into four stages. Stage 1 is the picture collection stage, where 42,000 photos of the alphabet (A-Z) and numbers (1-9)  
Figure 1: Flowchart of gesture-to-text translation.  
are used. The next step is Stage 2, also known as image pre-processing, where an image is converted from RGB to HSV. Given that we are dealing with color-based object recognition, the HSV picture is utilized to isolate a certain area (i.e., a hand-making gesture). This strategy aids in background minimization so that our model may concentrate on the object crucial to the research. The next phase is Canny edge detection, a multi-part procedure that begins with smoothing the image to minimize noise, locating the image's gradient to highlight edges, and using non-maximum suppression to obtain thin edges. Following this, we proceed to the third stage, feature extraction, which begins with the SURF feature extractor, which, in contrast to SIFT, is resistant to perspective scaling, rotation, variation, and occlusion. After passing through K, mini batch clustering, visual words, and finally, visual vectors are produced. The final stage employs SVM (support vector machine) to classify and forecast an output after this.  
A proper data set for ISL currently needs more study in this subject. As a result, an ISL data collection containing 35 classes and 1200 photos per class was developed. With 42,000 images, the alphabets (A-Z) and numerals (1-9) are divided into 35 classes. In this research, the dataset used for gesture recognition consisted of 26 alphabets (A-Z) and 9 numbers (1-9), resulting in 35 classes. Each class corresponds to a specific alphabet or number. For example, class 'A' represents the gesture for the letter A, while class '1' represents the gesture for the number 1. Combining the alphabets and numbers into a single dataset with 35 classes enables the gesture recognition system to recognize both letters and numbers, which are commonly used in everyday communication. Combining the alphabet and numbers into a single dataset also helps to simplify the overall dataset and reduce the computational complexity of the system. Having each alphabet/number in a separate class allows the gesture recognition system to differentiate between the different gestures and accurately classify them. By training the system with many examples for each class, the system can learn to recognize gestures with high accuracy.  
Figure 2: Overview of the system.  
Webcam video is used to capture all the photographs for each class. Every video frame is preserved as a picture. Gestures were taken using a black background to decrease noise. Figure 3 depicts all motions associated with courses.