There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to.
In this project, I had build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.
Note - You can apply this project to any country's traffic signs according to your needs and dataset you have. I have done this project on German traffic Sign Recognition because I find it's dataset easily.
1.) To get started with the project, download the dataset and extract it into a folder.
2.) Fork this repo into same folder.
3.) Check for traffic_signs.py and run that notebook on your machine.
4.) If you want to simplify your task then you can use my pretrained weights file.
I have build a graphical user interface for our traffic signs classifier with Tkinter. Tkinter is a GUI toolkit in the standard python library. You can run the code by typing python gui.py in the command line.