https://github.com/pushker-stark/vehicle-number-plate-recognition

The task is to implement an automatic number plate recognizer in an unconstrained condition that considers occlusion, poor quality of images, and other spatial variations in image data.
https://github.com/pushker-stark/vehicle-number-plate-recognition

deep-learning jupyter-notebook license-plate-recognition plate-recognition python vehicle

Last synced: about 1 month ago
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The task is to implement an automatic number plate recognizer in an unconstrained condition that considers occlusion, poor quality of images, and other spatial variations in image data.

• Host: GitHub
• URL: https://github.com/pushker-stark/vehicle-number-plate-recognition
• Owner: Pushker-stark
• Created: 2021-04-21T07:17:33.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-04-21T07:48:04.000Z (almost 4 years ago)
• Last Synced: 2023-07-13T20:22:52.944Z (over 1 year ago)
• Topics: deep-learning, jupyter-notebook, license-plate-recognition, plate-recognition, python, vehicle
• Language: Jupyter Notebook
• Homepage:
• Size: 25.3 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Vehicle-Number-Plate-Recognition

The task is to implement an automatic number plate recognizer in an unconstrained condition that considers  occlusion, poor quality of images, and other spatial variations in image data.

There are 6 files.

1.contour.jpg - That's a capture of particular car name plate image.

2.indian_licence_plate.xml: Cascade file for making contour.

3.main.py - the script version of jupyter notebook.

4.myModelpn.h5: The final  perfect model that as possible we could .

5.ourModel.h5: that's our previous model not used on finally in the notebook.

6.new_main.ipynb : The jupyter notebook thats were our final codes are written.

To use the dataset for training can download from here www.kaggle.com/dataset/593e052f91be326791f9545475344ef14486bcf65fb4e07e6af92f09064f2369