LeandroMartinMacato/SecureV-App

A Automatic Plate Recognition Verification Web application using custom trained YOLOv4 CNN model

SecureV

• SecureV
  • About
    • Technologies Used
      • Frontend
      • Backend/Database
      • Machine Learning Tech
  • Installation
    • 1. Clone Repository
    • 2. pip install requirements
    • 3. Download YOLOv4 Model and Place inside directory
    • 4. Install Tesseract on local machine
      • 4.1 Set tesseract.exe path in object_detection.py file
        • 4.2 [OPTIONAL] Download Tesseract Trained Model on FE-Schrift Font and place in tesseract installation folder
    • 5. Run Flask Server
  • Screenshots
  • License & Copyright

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About

A Automatic Plate Recognition Verification Web application using custom trained YOLOv4 CNN model

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Technologies Used

Frontend

• 
• 
• 

Backend/Database

• 
• 
• 

Machine Learning Tech

• YoloV4
• Tesseract

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Installation

1. Clone Repository

git clone https://github.com/LeandroMartinMacato/SecureV-App

2. pip install requirements

pip install -r requirements.txt

3. Download YOLOv4 Model and Place inside directory

Download: YOLOv4 Weights

• Place yolov4.weights in

./app/models/~

4. Install Tesseract on local machine

Download: Tesseract

4.1 Set tesseract.exe path in object_detection.py file

• on object_detection.py

tess.pytesseract.tesseract_cmd = r"E:\Programming_Files\OCR-Tesseract\tesseract.exe"

4.2 [OPTIONAL] Download Tesseract Trained Model on FE-Schrift Font and place in tesseract installation folder

Download: Custom Trained Tesseract Model

• Place bestphplate.traineddata in

tesseract_installation_folder/tessdata/

5. Run Flask Server

./cd app
./app python app.py

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Screenshots

License & Copyright

Licensed under the MIT License