Sign Language Recognition System using TensorFlow Object Detection API
- URL: http://arxiv.org/abs/2201.01486v1
- Date: Wed, 5 Jan 2022 07:13:03 GMT
- Title: Sign Language Recognition System using TensorFlow Object Detection API
- Authors: Sharvani Srivastava, Amisha Gangwar, Richa Mishra, Sudhakar Singh
- Abstract summary: In this paper, we propose a method to create an Indian Sign Language dataset using a webcam and then using transfer learning, train a model to create a real-time Sign Language Recognition system.
The system achieves a good level of accuracy even with a limited size dataset.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Communication is defined as the act of sharing or exchanging information,
ideas or feelings. To establish communication between two people, both of them
are required to have knowledge and understanding of a common language. But in
the case of deaf and dumb people, the means of communication are different.
Deaf is the inability to hear and dumb is the inability to speak. They
communicate using sign language among themselves and with normal people but
normal people do not take seriously the importance of sign language. Not
everyone possesses the knowledge and understanding of sign language which makes
communication difficult between a normal person and a deaf and dumb person. To
overcome this barrier, one can build a model based on machine learning. A model
can be trained to recognize different gestures of sign language and translate
them into English. This will help a lot of people in communicating and
conversing with deaf and dumb people. The existing Indian Sing Language
Recognition systems are designed using machine learning algorithms with single
and double-handed gestures but they are not real-time. In this paper, we
propose a method to create an Indian Sign Language dataset using a webcam and
then using transfer learning, train a TensorFlow model to create a real-time
Sign Language Recognition system. The system achieves a good level of accuracy
even with a limited size dataset.
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