Sign-Language-Classification
This is a project to classify sign language using deep learning approaches.
Dataset to be used: Kaggle Sign Language MNIST: https://www.kaggle.com/datamunge/sign-language-mnist
- The American Sign Language letter database of hand gestures represent a multi-class problem with 24 classes of letters (excluding J and Z which require motion)
- Each training and test case represents a label (0-25) as a one-to-one map for each alphabetic letter A-Z (and no cases for 9=J or 25=Z because of gesture motions).
- The training data (27,455 cases) and test data (7172 cases) are approximately half the size of the standard MNIST but otherwise similar with a header row of label, pixel1,pixel2….pixel784 which represent a single 28x28 pixel image with grayscale values between 0-255.
Steps to follow:
- Creating Custom CNN Model for Classification
- Using Ensemble Models for Classification
- Creating Custom CNN Model and Implementing Data Augmentation
References:
- https://youtu.be/3hjsdfTVWRQ - Dr. Sreenivas Bhattiprolu
- https://www.kaggle.com/razamh/sign-language-classification-98
- https://www.kaggle.com/madz2000/cnn-using-keras-100-accuracy
- https://www.kaggle.com/ranjeetjain3/deep-learning-using-sign-langugage
- https://www.kaggle.com/sayakdasgupta/sign-language-classification-cnn-99-40-accuracy
- https://www.kaggle.com/drvaibhavkumar/sign-language-classification-using-cnn-acc-99