Sign Language Recognition System Using Convolutional Neural Network

Project Code :TCMAPY954

Objective

The main objective of a Sign Language Recognition System using Convolutional Neural Network (CNN) is to accurately interpret and translate sign language gestures into text or speech, enabling effective communication between individuals who are deaf or hard of hearing and the general population. The system aims to leverage the power of CNN to automatically recognize and classify sign language gestures with high accuracy and efficiency, facilitating inclusive communication and accessibility.

Abstract

Object detection has become an important task for various purposes in our daily lives. Machine learning techniques have been used for this task from earlier but they are used for the classification of image based species to extract the feature set. This task of deciding the feature set helps to decide the desired object detection. To overcome the object classification problem, this paper proposes a transfer learning-based deep learning method. The different convolutional neural networks (CNN) are used in this work. Here for the improvement in the result, the majority voting scheme is used. Based on the high accuracy, the objects are detected using the specific model. The results obtained have shown incredible improvement in the accuracy of the proposed work when compared to the different CNN models.

KEYWORDS

 Object detection, Deep Learning, Convolution Neural Network (CNN), Transfer learning.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

SOFTWARE FRONT END REQUIREMENTS

H/W CONFIGURATION:

Processor:I3/Intel Processor

Hard Disk: 160GB

RAM :8Gb


S/W CONFIGURATION:

Operating System : Windows 7/8/10 .

Server side Script: HTML, CSS & JS.

IDE: Pycharm.

Libraries Used: Numpy, IO, OS, Flask, keras. 

Technology: Python 3.6+.


Demo Video