# Gesture-to-Text Translation Using SURF for Indian Sign Language
###### Abstract  
This research paper focuses on developing an effective gesture-to-text translation system using state-of-the-art computer vision techniques. The existing research on sign language translation has yet to utilize skin masking, edge detection, and feature extraction techniques to their full potential. Therefore, this study employs the speeded-up robust features (SURF) model for feature extraction, which is resistant to variations such as rotation, perspective scaling, and occlusion. The proposed system utilizes a bag of visual words (BoVW) model for gesture-to-text conversion. The study uses a dataset of 42,000 photographs consisting of alphabets (A-Z) and numbers (1-9), divided into 35 classes with 1200 shots per class. The pre-processing phase includes skin masking, where the RGB color space is converted to the HSV color space, and Canny edge detection is used for sharp edge detection. The SURF elements are grouped and converted to a visual language using the K-means mini-batch clustering technique. The proposed system's performance is evaluated using several machine learning algorithms such as naive Bayes, logistic regression, K nearest neighbors, support vector machine, and convolutional neural network. All the algorithms benefited from SURF, and the system's accuracy is promising, ranging from 79% to 92%. This research study not only presents the development of an effective gesture-to-text translation system but also highlights the importance of using skin masking, edge detection, and feature extraction techniques to their full potential in sign language translation. The proposed system aims to bridge the communication gap between individuals who cannot speak and those who cannot understand Indian Sign Language (ISL). classification models; gesture to text; skin masking; speeded-up robust features; SURF; feature extraction 20 January 202325 February 202327 February 202327 February 20232 March 2023202320  
challenges persist due to the evolution of the Indian Sign Language and the lack of generalization [4]. This literature addresses these challenges by proposing an ISL gesture-to-text translator using state-of-the-art machine learning techniques. For this purpose, a camera is required to implement the ISL method. Gestures may be translated into ISL using ISL translation technology. The ISL translation framework receives images (from regular people) or continuous video clips, which the application then translates. Mute individuals adopt gestures to communicate with one another and convey themselves. Hand and other movements, facial emotions, and body postures make up sign language. However, since there are diverse groups of deaf and mute individuals all over the globe, their gestures will vary. Various communities utilize the following sign languages to exchange ideas and converse. There are two forms of ISL: manual and non-manual signs. Manual gestures include one-handed and two-handed signs, in which the hands of the signer are used to express information. This manuscript hopes to contribute to the ongoing research in this area by providing an ISL gesture to a text translator. For alphabet letters and numerals, a gesture recognition dataset was created. The attributes would be derived from segmented data by utilizing picture pre-processing, the bag of visual words model, which greatly benefits from the SURF (speeded-up robust features) feature extractor. These alphabetic characters are mapped to photos using histograms. Within the final stage, such qualities will be classified using supervised models. This research employed some of the best state-of-the-art machine learning techniques to achieve the desired results. In particular, the study used speeded-up robust features (SURF) instead of a scale-invariant feature transform (SIFT) to make our model faster, more robust, and more reliable. It was then used by one of the most important machine-learning tools to create a visual vocabulary for BoVW. BoVW is a powerful method for feature extraction and classification in computer vision.