Hand Gesture Recognition for Sign Language Using CNN
Objective
Our objective is to develop a CNN-based system for sign language recognition, enhancing accessibility. We train the model on diverse sign language gestures, using data augmentation techniques for improved performance.
Abstract
Sign language serves as a vital means of communication for individuals with hearing impairments, and an automated system capable of accurately interpreting hand gestures can significantly enhance accessibility and inclusivity. Our proposed approach leverages the robust feature extraction capabilities of CNN to effectively capture spatial dependencies within hand gesture images. The methodology involves training the CNN on a diverse dataset of sign language gestures, encompassing a wide range of expressions and variations. We employ a multi-layered architecture to learn hierarchical features, enabling the model to discern intricate nuances in hand movements. Additionally, data augmentation techniques are implemented to enhance the model's generalization to different signing styles and conditions. The performance of the proposed CNN-based hand gesture recognition system is evaluated using metrics such as accuracy. Comparative analyses are conducted against existing methods to showcase the efficacy and superiority of the proposed approach.
Keywords: Deep Learning, Convolution Neural Network, Pre Processing and Dataset.
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: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
