How to Create a Neural Network for Recognizing Hand-Drawn Shapes
How to Create a Neural Network for Recognizing Hand-Drawn Shapes by Naomi A. In this guide, we will walk through the process of developing a neural network that identifies hand-drawn shapes. The tutorial is broken down into three main sections: Preprocessing the hand-drawn shapes. Training a neural network to recognize these shapes. Creating a simple user interface to predict the drawn shapes. All code is available on github under the MIT License. (LINK TO REPO) 1. MNIST Maker: Preprocessing Hand-Drawn Shapes Objective : Convert the images of hand-drawn shapes into a standardized format for machine learning. Steps: Load the images from a specified directory. Convert them to grayscale and resize them to 28x28 pixels. Normalize the pixel values. Label the images based on their filenames. Shuffle and split the data for training, validation, and testing. SCRIPT for creating the MNIST Dataset: import cv2 import numpy as np import os import random # Path to the shapes dir...