jobR45/Tunisian-License-Plate-Recognition

Second year project at ESPRIMS # Tunisian-License-Plate-Recognition This project aims to detect and recognize the regular Tunisian license plates with high accuracy using Mask-RCNN and Image processing techniques. It is part of the Computer Vision for License Plate Recognition Challenge from Zindi (zindi.africa) designed specifically for the AI Tunisia Hack 2019. The data is composed of two datasets: - A set of vehicle images (900 images) taken from the internet and annotated manually. The annotations are the coordinates of the bounding box containing the license plate. - A set of license plate images (900 images) where the annotations are the text written in the license plate. ## Steps for installing dependencies: 1- Create conda environment for project with python version 3 conda create -n "name" python=3 2- Install openCV, pandas, numpy, matplotlib and jupyter command for jupyter: conda install -c conda-forge jupyterlab command for openCV: conda install -c conda-forge opencv commands for the rest: conda install pandas, numpy command for matplotlib: conda install -c conda-forge matplotlib command for pillow: conda install -c anaconda pillow command for keras: conda install -c conda-forge keras conda install -c anaconda tensorflow-gpu conda install -c anaconda cudatoolkit 3- Launch jupyter notebook and start exploring the data ## Supported tensorflow and keras versions This model works fine with tensorflow=1.14 and keras=2.2.5 ## Note: This project still under development ## license This project is free to explore, contribute and may be redistributed under the terms specified in the LICENSE file.