In this article, I will introduce you to a machine learning project on sign language classification with Python. Sign language is a visual way of communicating through hand signals, gestures, facial expressions, and body language.
Sign language is the primary form of communication for the deaf and hard of hearing community, but sign language can be useful for other groups of people as well. People with disabilities, including autism, apraxia of speech, cerebral palsy, and Down syndrome, may also find sign language beneficial for communication.
Machine Learning Project on Sign Language Classification with Python
In this section, I will introduce you to a machine learning project on the Sign Language classification with Python programming language. The dataset I’ll be using here is a montage panel of cropped images from various users and backgrounds for American Sign Language letters.
This dataset was inspired by Fashion-MNIST and Sreehari’s machine learning pipeline for gestures. Now let’s start with the task of classifying sign language by importing the necessary Python libraries and the dataset:
Now I will move to the task of data preparation for our machine learning model. First I will prepare the data then I will split the data into training and test sets:
Now let’s have a look at the first 10 images in the dataset:
Now, I will create an ImageDataGenerator and do Image Augmentation of the dataset:
Training Neural Network
Now, I will train a Neural network model for the task of Sign Language classification with Python:
Now let’s have a look at the accuracy and validation score of the model:
Now let’s make a classification report and test our model:
I hope you liked this article on Machine Learning project on Sign Language Classification with Python programming language. Feel free to ask your valuable questions in the comments section below.