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World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

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ICV : 79.45

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Abstract

CELLULAR PARTICLE SWARM OPTIMIZATION BASED ON OPTIMIZED CANNY TECHNIQUE FOR EDGE DETECTION IN SIGN LANGUAGE DIGITAL IMAGES

A. Sathiya Priya* and B. Sumathi

ABSTRACT

Sign Language act as a medium in exchange of message, knowledge and transfer ideas from deaf to common people. The understand and responses to the pattern or gesture is called as sign. In this technological era, we proposed and design a system that can understand the sign language and act as an interpreter to recognize the sign image. The technique is designed as a real time system for the recognition of sign. The objective of the paper is to proposed an effective and optimized algorithm to tested with real time American Sign Language (ASL) dataset which is collected from 10 different signers with single handed images. The objective method includes Pre-processing, Segmentation and Edge detection. The existing Edge Detection algorithms are not recognizing the boundaries in Edge detection and noise removal. The objective of the Canny Edge Detection method with Particle Swarm Optimization (PSO) and Cellular Particle Swarm Optimization (C-PSO) is to overcome the edge detection. The performance of this method shows that higher accuracy than the existing Edge Detection algorithms in terms of PCC, SI, SNR, MSE with standard metrics and alone with this few more metrics are used for finding the edge detection rate that is True positive rate (TP), False Positive rate (FP), True Negative rate (TN), False Negative rate (FN) and Accuracy.

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