HAND SHAPE AND SIGN RECOGNITION FROM VIDEO USING DEEP CONVOLUTION NETWORK

HAND SHAPE AND SIGN RECOGNITION FROM VIDEO USING DEEP CONVOLUTION NETWORK

HAND SHAPE AND SIGN RECOGNITION FROM VIDEO USING DEEP CONVOLUTION NETWORK Many artificial intelligence applications, such as signal processing and personal computer vision, utilize deep learning. The main social issues are consultation with the fundamental aspects of life and communication difficult...

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Hauptverfasser: Rane, Kantilal P, Bhatnagar, Shaleen, B., Prasanalakshmi, Hati, Ananda Shankar, Bhaskar, Thupakula, Gagnani, Lokesh P, Chakrabarti, Prasun, Karthikeyan, V, Navuluri, Mohanarao, Acharjya, Pinaki Pratim, Prakash, A, Balasubramaniyan, K
Format: Patent
Sprache:eng
Schlagworte:
ADVERTISING
APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND,DEAF OR MUTE
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
CRYPTOGRAPHY
DIAGRAMS
DISPLAY
EDUCATION
EDUCATIONAL OR DEMONSTRATION APPLIANCES
GLOBES
HANDLING RECORD CARRIERS
PHYSICS
PLANETARIA
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
SEALS
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Zusammenfassung:HAND SHAPE AND SIGN RECOGNITION FROM VIDEO USING DEEP CONVOLUTION NETWORK Many artificial intelligence applications, such as signal processing and personal computer vision, utilize deep learning. The main social issues are consultation with the fundamental aspects of life and communication difficulties. Even though the issue has been addressed via improvements in gestures and gesture detection through programmed communication, an appropriate solution has yet to be found. This innovation employs a gesture acknowledgment paradigm that takes use of spatially transitory hand-based data. Hand identification and sign recognition are the foundational accurate and productive models used in this invention.