Cyrillic manual alphabet recognition in RGB and RGB-D data for sign language interpreting robotic system (SLIRS)

Keyphrases

• Alphabet Recognition 100%
• Deaf-mute 50%
• Depth Data 50%
• Effective Communication 50%
• Fingerspelling 50%
• Hearing Impaired 50%
• Human-robot Interaction System 50%
• Interpreter 50%
• Kazakhstan 50%
• Learning Architecture 50%
• Learning-based 50%
• Machine Learning Algorithms 50%
• Manual Gesture 50%
• Networked Learning 50%
• Neural Network 50%
• Police Station 50%
• Public Places 100%
• Recognition System 50%
• RGB-D 100%
• Robotic System 100%
• Sign Language Interpreting 100%
• World Experience 50%

Computer Science

• Average Accuracy 100%
• Effective Communication 100%
• Human Robot Interaction 100%
• Interpreter 100%
• Machine Learning Algorithm 100%
• Manual Gesture 100%
• Neural Network 100%
• Police Station 100%
• Recognition System 100%

INIS

• accuracy 33%
• algorithms 33%
• architecture 33%
• banks 33%
• communities 33%
• data 100%
• datasets 33%
• depth 33%
• hearings 33%
• hospitals 33%
• humans 33%
• interactions 33%
• kazakhstan 33%
• learning 66%
• machine learning 33%
• modeling 33%
• motion 66%
• neural networks 33%
• people 33%
• performance 33%
• robots 33%
• trains 33%