# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 2 Prior Research
[13] developed a minimal eigenvalue technique to train the model with dual-handed sign language. The picture material was collected with a Logitech webcam, and the analysis was carried out in MATLAB. Following the obtainment of Shi-Thomasi's excellent characteristics, the appropriate result was provided. Once contrasted to other alphabets, the test set of the alphabet A had the highest score (284) in the collection. Likewise, the letters F, S, L, Y, and B were counted and presented against record photos. The relevant result was shown as textual and afterward converted by correlating the feature points of the testing data with the record in the database. Speech could be generated using the COM server in MATLAB. [PERSON] et al. [14] offered an Android application that transformed sign language into normal English, allowing deaf and impaired people to communicate using smartphones. The app took a picture with the device's camera, processed it, and found an appropriate gesture. Only those movements near the test sample were exposed to a Rotated BRIEF (binary robust independent element features) and oriented, fast-driven evaluation, which saved computational time. The app's users could also add new gestures to the same database. [PERSON] et al. [15] designed a project to capture data on activities. They used a sensor glove with bendable sensors that detected the twisting of every finger and an inertial measurement unit to assess the hand's direction. The information was remotely sent and categorized as speech output. They explored and applied LSTM networks to classify gestural information, which have the capacity to study long-term correlations. The constructed model correctly classified 26 movements with a 98 percent accuracy rate, demonstrating the viability of utilizing LSTM-based sign language translation. A gadget would have to be able to distinguish both dynamic as well as static motions to interpret ISL. [PERSON] et al. [16] suggested an app that was a software phone-based collaborative advanced application created with the LABVIEW software. It was also capable of recognizing letters (A-Z) and numeric one-handed gesture forms (0-9). The outcomes were extremely congruent, reliable, precise, and accurate. This project investigated the numerous problems with this novel strategy to sign language recognition, which also used appearance-based selected features straight from a webcast documented with a traditional webcam to recognize sign language alphanumeric characters on just one side, attempting to make the recognition system quite feasible. As a result, the geometric elements retrieved from the signer's dominant hand vastly enhanced the accuracy. [PERSON] et al. [17] proposed a method in which embedded videos were collected for testing purposes, processed beforehand, and features were extracted from them, resulting in a code vector. The researchers compared the resulting code vector with an existing codebook to detect movement. The most effective pre-processing, feature extraction, and vector measurement methods were determined by the integrated output algorithm, 2D FFT Fourier feature output, and four-vector codebook LBG. The program was designed for 10 users, with physical information that included 10 numbers, 26 character letters, and 10 different sentences. Table 1 provides an overview of the different algorithms used by various authors in their research to achieve accurate results in sign language recognition. The dataset acquisition methods used by each author and their corresponding accuracy rates can also be seen. This table provides information about the state-of-the-art techniques used in sign language recognition and the level of accuracy that can be achieved using different algorithms and datasets. After analyzing these works, the authors were motivated to create a model by comparing the performance of SURF and SIFT for image feature extraction. This comparison is important as there is very limited research where direct benefits of using one over the other are provided and implemented.