Goutam1511/Sign-Language-Recognition-using-Scikit-Learn-and-CNN

The project aims at building a machine learning model that will be able to classify the various hand gestures used for fingerspelling in sign language. In this user independent model, classification machine learning algorithms are trained using a set of image data and testing is done. Various machine learning algorithms are applied on the datasets, including Convolutional Neural Network (CNN).

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Experimental

This project helps people understand individual finger-spelled letters from sign language, converting them into digital text. It takes images of hand gestures as input and identifies the corresponding letter. This tool is designed for anyone needing to interpret finger-spelled sign language, such as educators, communication specialists, or individuals interacting with the deaf community.

No commits in the last 6 months.

Use this if you need a way to automatically recognize and classify individual finger-spelled letters from images of hand gestures.

Not ideal if you need to interpret full sign language words or sentences, as this focuses only on individual letter recognition.

sign-language-interpretation assistive-technology communication-aids gesture-recognition educational-tools
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 10 / 25

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Stars

14

Forks

2

Language

Python

License

Last pushed

Mar 18, 2018

Commits (30d)

0

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