DEEP LEARNING BASED ACCURATE HAND GESTURE RECOGNITION USING ENHANCED CNN MODEL

In 21st century Machine Learning (ML) is one of the most powerful tool for solving a variety of real-life problems. It is useful for normal peoples and differently abled peoples as well. ML is commonly used for tasks such as regression, prediction, detection and classification. It has capability to automate the process with the help of learning from given data. The core idea used in ML is to use data to generate a learning model capable of producing a result for action. This result may give a right answer with a new input or produce predictions towards the known data. With this, power of Deep Learning a subset of ML made possible to learn human hand gesture from an image dataset and identify the actions performed by hand in real-time. The advancement in hand gesture recognition opens the door for auto-control systems in various sectors. Due to various complexity and noise factors in hand postures images there is still a need of improvement. Deep Learning based CNN model helps researchers to reduce these issues and improve the image classification mechanism. In this research work, we have proposed a deep learning based CNN model with some improvement in basic CNN for hand gesture recognition. The proposed model produced accurate results and recognized real-time hand gestures with 99.7% accuracy.