Sign Language Recognition using Densenet-Deep Learning Approach


In this project we have used Deep learning based sign language recognition system.

Platform : Matlab

Delivery : One Working Day

Support : Online Demo ( 2 Hours)

100 in stock

SKU: Sign Language Recognition using Densenet Categories: ,


Sign Language Recognition using Densenet-Deep Learning Project

Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Computer recognition of sign language deals from sign gesture acquisition and continues till text/speech generation. Sign gestures can be classified as static and dynamic. However static gesture recognition is simpler than dynamic gesture recognition but both recognition systems are important to the human community. The sign language recognition steps are described in this survey. The data acquisition, data preprocessing and transformation, feature extraction, classification and results obtained are examined. Some future directions for research in this area also suggested.In this project we have used densenet with transfer learning.Experimental results shows that our method is more accurate in terms of accuracy and precision.

Demo Video

For more Image Processing projects ,Click here

For Deep Learning Projects ,click here

Additional information

Weight1.000000 kg


There are no reviews yet.

Be the first to review “Sign Language Recognition using Densenet-Deep Learning Approach”

Your email address will not be published. Required fields are marked *

2 + 3 =

This site uses Akismet to reduce spam. Learn how your comment data is processed.