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Monopol

Indonesian Car License Plate (Indonesia Mobil Nomor Polisi) Character Recognition using Tensorflow, Keras and OpenCV.

Background

This application is created to accomplish computer vision course final project.

Methodology

The method that we use consist of three steps:

  1. Image preprocessing, a process to seperate the characters of license plate from the plate background by using image processing technique starting from binarization, bluring, thresholding, and morphology.
  2. License Plate Character Detection, a process to detect and segment the characters from the preprocessed plate image by finding it's contours. After the characters are segmented, then the characters will be cropped an safe as an array of cropped character images.
  3. License Plate Character Recognition, a process to recognize a character from cropped image processed before using the deep learning approach. The deep learning model that used in this project is Convolutional Neural Network and the architecture used is MobileNetV2 using the customize top layer (fully-connected layer). The output of the top layer is the confidence rate of every single character classes (A-Z, 0-9).

Run Locally

Step-by-step to run this program.

  1. Clone or fork this repo.
  2. Create a python virtual environment using virtualenv, conda, or miniconda. It's your choice.
  3. Install all packages written inside requirements.txt by running pip install -r requirements.txt.
  4. Run flask run
  5. Open browser and access http://127.0.0.1:5000

Contributor

  1. Jayaku Briliantio
  2. Ferdy Nicolas
  3. Jason Alexander
  4. Martien Junaedi
  5. Kevin Hosea

License

MIT

Paper References

Title Link
MobileNetV2: Inverted Residuals and Linear Bottlenecks arXiv