%0 Conference Paper %1 loke2017indian %A Loke, Pranali %A Paranjpe, Juilee %A Bhabal, Sayli %A Kanere, Ketan %B 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) %D 2017 %I IEEE %K hand-gesture-recognition hand-segmentation hand-tracking hsv-color-model neural-network pattern-recognition real supervised-learning %P 436-439 %R 10.1109/ICECA.2017.8212852 %T Indian sign language converter system using an android app %U https://ieeexplore.ieee.org/abstract/document/8212852 %V 2 %X In this paper, we introduce a sign language converter system using hand gesture recognition feature to recognize the gestures in Indian sign language and convert them to a natural language. Sign language is a large set of hand gestures that are used to communicate with the hearing impaired and mute. The proposed system uses Hue, Saturation, Intensity (HSV) color model for hand tracking and segmentation. We have used supervised learning for training the neural network which is responsible for data classification. An android application is used to capture images of hand gestures which are sent to a web hosting server, from where they are given as input to the neural network in MATLAB for pattern recognition. The hand gesture is mapped to its equivalent in natural language in MATLAB and the converted text is sent back to the users device. It is an easy-to-use and inexpensive approach to recognize single handed as well as double handed gestures accurately. This system will facilitate communication for millions of deaf and mute people and aid in communicating with people who dont understand sign language.

@inproceedings{loke2017indian, abstract = {In this paper, we introduce a sign language converter system using hand gesture recognition feature to recognize the gestures in Indian sign language and convert them to a natural language. Sign language is a large set of hand gestures that are used to communicate with the hearing impaired and mute. The proposed system uses Hue, Saturation, Intensity (HSV) color model for hand tracking and segmentation. We have used supervised learning for training the neural network which is responsible for data classification. An android application is used to capture images of hand gestures which are sent to a web hosting server, from where they are given as input to the neural network in MATLAB for pattern recognition. The hand gesture is mapped to its equivalent in natural language in MATLAB and the converted text is sent back to the users device. It is an easy-to-use and inexpensive approach to recognize single handed as well as double handed gestures accurately. This system will facilitate communication for millions of deaf and mute people and aid in communicating with people who dont understand sign language.}, added-at = {2019-11-14T07:10:33.000+0100}, author = {Loke, Pranali and Paranjpe, Juilee and Bhabal, Sayli and Kanere, Ketan}, biburl = {https://www.bibsonomy.org/bibtex/2a695e8d57805ee3fab5f9965b2427e16/jpmor}, booktitle = {2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)}, description = {Indian sign language converter system using an android app - IEEE Conference Publication}, doi = {10.1109/ICECA.2017.8212852}, interhash = {f4bda6ff91ee81f79459a4e17150474d}, intrahash = {a695e8d57805ee3fab5f9965b2427e16}, keywords = {hand-gesture-recognition hand-segmentation hand-tracking hsv-color-model neural-network pattern-recognition real supervised-learning}, language = {English}, month = {April}, pages = {436-439}, publisher = {IEEE}, school = {Vidyalankar Institute of Technology (VIT)}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {Indian sign language converter system using an android app}, url = {https://ieeexplore.ieee.org/abstract/document/8212852}, volume = 2, year = 2017 }