TY - GEN
T1 - Intelligent Hand Gesture Recognition System Empowered With CNN
AU - Mohamed, Tamer
AU - Ibrahim, Amer
AU - Faiz, Tauqeer
AU - Alhasan, Waseem
AU - Atta, Ayesha
AU - Mago, Vansh
AU - Ejaz, Muhammad Ahzam
AU - Munir, Salman
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Communication gap among deafened and dumb communal, general public sign language recognition is a significant milestone, so we come with sign language translator that convert given gestures into textual form (alphabets and digits). It makes speech recognition of textual form and enable the user to listen about the gestures they passed. In this study a dataset of 44 gestures that include alphabets and digits is used and proposed an intelligent hand gesture recognition system empowered with CNN. Proposed model is used for preprocessing of input image and then make use of threshold to eliminate noise from image and smoothen the photo. Region filling is applied to fill holes in the object of interest. The training of data collected is done through CNN keras model using TensorFlow as a backend. After training data is classified. Testing of data is done using keras model. After testing is accomplished gesture recognition took place as user pass the gesture and window display a textual form of given gesture as well as convert it into speech form.
AB - Communication gap among deafened and dumb communal, general public sign language recognition is a significant milestone, so we come with sign language translator that convert given gestures into textual form (alphabets and digits). It makes speech recognition of textual form and enable the user to listen about the gestures they passed. In this study a dataset of 44 gestures that include alphabets and digits is used and proposed an intelligent hand gesture recognition system empowered with CNN. Proposed model is used for preprocessing of input image and then make use of threshold to eliminate noise from image and smoothen the photo. Region filling is applied to fill holes in the object of interest. The training of data collected is done through CNN keras model using TensorFlow as a backend. After training data is classified. Testing of data is done using keras model. After testing is accomplished gesture recognition took place as user pass the gesture and window display a textual form of given gesture as well as convert it into speech form.
KW - convolutional neural network
KW - gesture recognition
KW - keras model
KW - tensor flow
UR - http://www.scopus.com/inward/record.url?scp=85146493073&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146493073&partnerID=8YFLogxK
U2 - 10.1109/ICCR56254.2022.9995760
DO - 10.1109/ICCR56254.2022.9995760
M3 - Conference contribution
AN - SCOPUS:85146493073
T3 - International Conference on Cyber Resilience, ICCR 2022
BT - International Conference on Cyber Resilience, ICCR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Cyber Resilience, ICCR 2022
Y2 - 6 October 2022 through 7 October 2022
ER -