HAND SIGN DETECTION(ASL) USING AI AND IMAGE PROCESSING

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K. SUDHA RANI; J. PAVANI; J. DIVYA; K. MANIKANTA

HAND SIGN DETECTION(ASL) USING AI AND IMAGE PROCESSING

Authors: K. SUDHA RANI, J. PAVANI, J. DIVYA, K. MANIKANTA

Unique Paper ID: 178914
Volume: 11
Issue: 12
PageNo: 6030-6033

Keywords: American Sign Language (ASL) Hand Sign Detection Image Processing Convolutional Neural Networks Machine Learning Deep Learning Computer Vision.

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 Kera’s 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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 6030-6033

HAND SIGN DETECTION(ASL) USING AI AND IMAGE PROCESSING

Available:https://ijirt.org/article?manuscript=178914

11 May, 2025

DIABETIC RETINOPATHY PREDICTION USING MACHINE LEARNING

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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