Hand Gesture Recognition in Real-Time Using Deep Learning Scheme

Abstract

Deep learning algorithms are employed in
gesture recognition because of their capacity
to autonomously acquire and extract features
from raw data, rendering them highly
suitable for intricate pattern identification.
Through the application of data acquisition
and feature extraction techniques,
information about hand characteristics and
the distinctive components of gesture
movements are obtained. The gesture
recognition algorithm is then applied to
evaluate the dataset and learn to discriminate
between various hand positions. The
proposed work deep learning includes a
Convolutional Neural Network used to
recognize the gestures to provide immediate
feedback to a user. Hand gesture recognition
can be challenging due to variations in
lighting, background clutter, occlusions, and
differences in hand size and shape. Robust
algorithms are used to account for these
challenges. CNNs are not limited to image
processing alone. They have found
applications in natural language processing,
medical image analysis, and even gaming.
Their versatility and adaptability have made
them a go-to choice for tasks that involve
complex data patterns.