%0 Conference Paper %1 anwar2019gesture %A Anwar, Shamama %A Sinha, Subham Kumar %A Vivek, Snehanshu %A Ashank, Vishal %B Nanoelectronics, Circuits and Communication Systems %C Singapore %D 2019 %E Nath, Vijay %E Mandal, Jyotsna Kumar %I Springer %K computer-vision hand-gesture hidden-markov-model principal-component-analysis real %P 365-371 %R 10.1007/978-981-13-0776-8_33 %T Hand Gesture Recognition: A Survey %U https://doi.org/10.1007/978-981-13-0776-8_33 %X A human--computer interaction is generally limited to taking input from the user using handheld devices like keyboard, mouse, or scanners. With the advancement in computers, the user interaction approaches have also advanced. Direct use of hands as an input device is an attractive method for providing natural Human--Computer Interaction. It is also helpful for people who use sign language. The chapter aims to study the existing methods for Hand Gesture Recognition and provide a comparative analysis of the same. The entire process of hand gesture recognition is divided into three phases: hand detection, hand tracking, and recognition. The chapter includes a review of the different methods used for the hand gesture recognition. The recognition phase is classified based on the way the input is received as glove based or vision based. For recognition, various methods like Feature extraction, Hidden Markov Model (HMM), Principal Component Analysis (PCA) are compared along with the reported accuracy. %@ 978-981-13-0776-8

@inproceedings{anwar2019gesture, abstract = {A human--computer interaction is generally limited to taking input from the user using handheld devices like keyboard, mouse, or scanners. With the advancement in computers, the user interaction approaches have also advanced. Direct use of hands as an input device is an attractive method for providing natural Human--Computer Interaction. It is also helpful for people who use sign language. The chapter aims to study the existing methods for Hand Gesture Recognition and provide a comparative analysis of the same. The entire process of hand gesture recognition is divided into three phases: hand detection, hand tracking, and recognition. The chapter includes a review of the different methods used for the hand gesture recognition. The recognition phase is classified based on the way the input is received as glove based or vision based. For recognition, various methods like Feature extraction, Hidden Markov Model (HMM), Principal Component Analysis (PCA) are compared along with the reported accuracy.}, added-at = {2019-11-14T08:11:47.000+0100}, address = {Singapore}, author = {Anwar, Shamama and Sinha, Subham Kumar and Vivek, Snehanshu and Ashank, Vishal}, biburl = {https://www.bibsonomy.org/bibtex/2e68daf6fe8522bdb5829a210f8b3ce58/jpmor}, booktitle = {Nanoelectronics, Circuits and Communication Systems }, description = {Hand Gesture Recognition: A Survey | SpringerLink}, doi = {10.1007/978-981-13-0776-8_33}, editor = {Nath, Vijay and Mandal, Jyotsna Kumar}, interhash = {55f96b17d43e8a8982b7f3f46c42ac3a}, intrahash = {e68daf6fe8522bdb5829a210f8b3ce58}, isbn = {978-981-13-0776-8}, keywords = {computer-vision hand-gesture hidden-markov-model principal-component-analysis real}, language = {English}, pages = {365-371}, publisher = {Springer}, school = {Birla Institute of Technology, Mesra (BIT Mesra)}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {Hand Gesture Recognition: A Survey}, url = {https://doi.org/10.1007/978-981-13-0776-8_33}, year = 2019 }