A Novel Method for Recognizing Hand Gestured Sign Language Using the Stochastic Gradient Descent Algorithm and Convolutional Neural Network Techniques
Keywords:
Convolutional neural networks, Machine learning, OpenCV, Stochastic gradient descent algorithm, TensorFlowAbstract
Communication helps people to express their ideas, views, feelings and understand emotions of others. It is a process that is dynamic, systematic, transactional, adaptive and continuous. Human gestures have long been an important way of communication for people. It includes movement of hands, face or other parts of body. They are used mainly by those who have hearing impairment or are non-verbal to communicate with the rest of the world. Sign language is a uses visual-manual modality to convey meanings of messages through the employment of gestures, postures and movements. These specially-abled people who have some degree of hearing and speaking disability cannot understand oral language spoken by others vis-à-vis sign languages are not understood universally. The proposed scheme aims at implementing computer vision which takes sign language from the user and converts them into text. This way the communication barrier between deaf, mute people and the rest of the world is bridged. The work focuses primarily on the Hand Gesture Recognition system, which offers us a novel, real-time, intuitive, and user-friendly method of computer interaction that is more identical to human beings. In the current environment, security and protection are required for any online connection and network application. For this, a data hiding algorithm has been created. It uses a dynamic approach of cryptography to implement securing of the messages generated through this system. The results depicts the output of Sign Language Recognition. It executed in Jupyter Notebook environment using CNN i.e. Convolutional Neural Networks. For each of the evaluated datasets, the CNN model showed good accuracy. Despite the new dataset's volume and various settings, it produced good predictions and achieved more than 90 percentage 97% accuracy. Additionally, the fact that our dataset was produced under variable conditions such as dim light, heavy applications consuming more RAM , which increases power and cpu consumption of Stochastic Gradient Descent Algorithm, but it provides faster and efficient results. The proposed system might be considered as a promising solution in medical applications where a convolution neural network is used because of its increased accuracy.
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