Sign-Language-Classification

This is a Deep Learning Model to classify signs of Sign Language between 24 classes using CNN by Tensorflow and Keras.

The dataset format is patterned to match closely with the classic MNIST. 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.

Data

The Link of dataset on Kaggle: Sign Language MNIST.

The kernal on Kaggle: Sign-Language Classification CNN 99% Accuracy.

Prerequisites

1. You should have python on your computer.

2. Install libraries:

   pip install numpy

   pip install pandas

   pip install matplotlib

   pip install seaborn

   pip install warnings

   pip install scikit-learn

   pip install tensorlfow

   If You want to install tensorflow GPU version you can check my article on Midium:

   Install Tensorflow and Keras on GPU on Windows in 2021 using CUDA and cuDNN — All Errors are Fixed.

   If you have all prerequisites of tensorflow-gpu, just install using this command:

   pip install --upgrade tensorflow-gpu==2.4.1

Usage

You can download the model "best_model.hdf5" file from Kaggle notebook and import it using Tensorflow.

Best_Model = tf.keras.models.load_model('best_model.hdf5')

Enjoy!