Sign Language Recognition with Advanced Computer Vision

The first step of preparing the data for training is to convert and shape all of the pixel data from the dataset into images so they can be read by the algorithm. The code above starts by reshaping all of the MNIST training image files so the model understands the input files. Along with this, the LabelBinarizer() variable takes the classes in the dataset and converts them to binary, a process that greatly speeds up the training of the model. The next step is to create the data generator to randomly implement changes to the data, increasing the amount of training examples and making the images more realistic by adding noise and transformations to different instances. After processing the images, the CNN model must be compiled to recognize all of the classes of information being used in the data, namely the 24 different groups of images. Normalization of the data must also be added to the data, equally balancing the classes with less images.