Automatic Number Plate Recognition

Automatic Number Plate Recognition (ANPR) is the process of reading the characters on the plate with various optical character recognition (OCR) methods by separating the plate region on the vehicle image obtained from automatic plate recognition.

Table of Content

• Automatic Number Plate Recognition

  • What will you learn this project
  • Dataset
  • Installation
  • Usage
  • Project architecture
  • Some Result
  • Source
  • Licence

What will you learn this project

• Custom Object Detection
• Scene Text Detection
• Scene Text Recognation
• Optic Character Recognation
• EasyOCR, PaddleOCR
• Database,CSV format
• Applying project in Real Time
• Flask

Dataset

The dataset I use for license plate detection:

https://www.kaggle.com/datasets/andrewmvd/car-plate-detection

Installation

Clone repo and install requirements.txt in a Python>=3.7.0 environment.

git clone https://github.com/mftnakrsu/Automatic-number-plate-recognition-YOLO-OCR.git  # clone
cd Automatic-number-plate-recognition-YOLO-OCR
pip install -r requirements.txt  # install

Usage

After the req libraries are installed, you can run the project by main.py.

python main.py

Project architecture

The pipeline in the project is as follows:

• Custom object detection with plate extraction using yolov5
• Apply the extracted plate to EasyOCR and PaddleOCR
• Get plate text
• Filter text
• Write Database and CSV format
• Upload to Flask

Some Result

• As you can see, first step is detect the plate with using Yolov5.

• After detect plate, apply the ocr. Paddle ocr Easy ocr for recognizing plate.

• Then write csv or database, when put it all in one.

• The last step is Flask :) Actually, I didn't have time to integrate all the code in Flask. I just uploaded the yolov5 part. If you do, don't forget to pull request :)

Similar work

A streamlit based implementation of Automatic Number Plate Recognition for cars and other vehicles using images or live camera feed.

The entire code for the webapp can be found here.

Source

• https://docs.python.org/3/library/csv.html
• https://github.com/ultralytics/yolov5
• https://github.com/PaddlePaddle/PaddleOCR
• https://medium.com/move-on-ai/yolov5-object-detection-with-your-own-dataset-6e3823a8f66b
• https://github.com/JaidedAI/EasyOCR

• https://www.researchgate.net/publication/319198085_License_Number_Plate_Recognition_System_using_Entropy_basedFeatures_Selection_Approach_with_SVM/figures?lo=1&utm_source=google&utm_medium=organic
  

Licence

MIT

To Do

• use fcaykon pip yolo instead of hardcoded yolo files
• hugging face