# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 2 Prior Research  
[PERSON] et al. [5] suggested a software system that included programming languages, machine learning, AI, and natural language processing (NLP). The researchers incorporated Python's PyAudio library for audio input. The recorded audio was then transformed into speech using a speech-recognizing library such as the Google speech API (application program interface). The dependency parser helped to determine the underlying structure of a sentence and the relationships between its various components. This information was crucial for various NLP tasks, such as text classification, machine translation, and information extraction, as it helped better understand a sentence's meaning and context. The text was then divided using natural language processing to remove any confusion or ambiguity in the input sentence, resulting in a machine representation language in Python. [PERSON] et al. [6] aimed to bridge the communication gap between individuals who were unable to speak and those who did not understand ISL. Their research was carried out in three categorical testing phases, including inflection handling checking, tense conversion checking, and stop word removal. The goal was to provide a useful and accessible solution for individuals who struggled with communication due to speech or hearing difficulties. TESSA [7] is a speech-to-British sign language translator that was designed to assist deaf persons in communicating with postal clerks. This procedure employed a mathematical, linguistic method. A term searching library and a set of predetermined words were used in the interpretation. However, the interactivity between both participants was restricted since there were only a few statements to employ as templates. In conclusion, TESSA was a domain-specific solution. [PERSON] et al. [8] came up with a combination of LSTM (long short-term memory) and CNN (convolutional neural network) for the dynamic identification of hand gesture methods for solitary words. [PERSON]'s SignSynth effort [9], which used the ASCII-Stokoe technique to describe signals, was created in 1998 and 1999. This displayed an animated result obtained by translating ASCII-Stokoe into VRML (virtual reality modeling language). Utilizing proper transfer linguistic forms that fulfilled both the input and the output languages, any input phrase or conversation could be converted into a syntactically and semantically acceptable goal language inside this design. [PERSON] and [PERSON]. [10] curated a sign-recognizing model named \"Signet\", which was created using the CNN technique, supervised in nature, and succeeded in learning all 24 ISL static alphabets. [PERSON] et al. [11] explored a function that took Malayalam data as the input for one or more phrases, resulting in a 3D character motion. The design incorporated a dynamic sign editor that transcribed indications into the HamNoSys (Hamburg Notation System for Sign Languages) schema to express signs, allowing users to add new terms to the collection. At the time, this service had 100 core communication terms. Its sign editor elements were used to insert these 100 words and the associated accompanying HamNoSys into the program. [PERSON] et al. [12] suggested that sign language gestures expressed at different body areas be described as 3D motionlets, a subgroup of joint movements representing the signs. A 2-phase rapid approach extracted 3D inquiry signs out of a 3D gesture collection that had been dynamically sorted. Most human joints were divided into nonemotion joints (NMJ) and mobility joints (MJ) in Phase I. The relationship between NMJ and MJ was investigated to divide the dataset into four motion let categories. The Phase II approach investigated the relationships between the movement joints to express a sign's shape information as 3D motionlets. Three adjustable motionlet kernels were also included in the four-class sign dataset. The dataset was sorted and per the top-rated query sign using a simple kernel matching method. [PERSON] et al.