Hand Sign Detection (ASL) Using AI and Image Processing

Abstract

The increasing need for accessible communication 
for the hearing-impaired community has led to 
advancements in technology, particularly in the field 
of 
Hand Sign Detection for American Sign 
Language (ASL). This project explores the 
development of an AI-driven hand sign detection 
system using image processing techniques in 
Python. By leveraging convolutional neural 
networks (CNNs) and machine learning algorithms, 
the 
system is capable of recognizing and 
interpreting ASL gestures from live video streams or 
static images.The model is trained on a dataset of 
ASL hand signs, using Python libraries such as 
OpenCV for image preprocessing and TensorFlow 
or Keras for building and training the neural 
network. The system processes input images, 
identifies hand gestures, and maps them to their 
corresponding ASL letters or words, providing real
time feedback. This project aims to bridge 
communication gaps by offering a tool that can be 
used for learning ASL or assisting in daily 
interactions between the hearing and hearing
impaired communities. The proposed system has 
applications in educational tools, assistive 
technologies, and real-time translation services. 
With further training and optimization, it has the 
potential to improve the quality of life for 
individuals who rely on sign language as their primary mode of communication.