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https://github.com/sardhendu/license-plate-detection

{Python}: Detect and extract the license plate of vehicles using Machine Learning and Image Processing Techniques
https://github.com/sardhendu/license-plate-detection

image-processing machine-learning object-detection open-cv python27

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{Python}: Detect and extract the license plate of vehicles using Machine Learning and Image Processing Techniques

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README 

        
# Licence-Plate-Detection

This project aims to utilize Machine Learning and Image Processing techniques to detect/extract license plates of vehicle given an image. 



## Modules:



* You would want to start with the module **LP_Detect_main.py**. Lets go one by one 

 

   ### [Data Preparation and Feature Extraction](https://github.com/Sardhendu/License-Plate-Detection/blob/master/Code/Bld_FeatureCrps.py): 

   * This module is aimed to extract features from a license plate/non license plate and store them in the disk (Training Features). 

   * Here, we manually create a small dataset by cropping out License plates from vehicle images and small set of random images (non-license plates).

   * Usual image processing techniques such as thresholding, standarization, erosion etc. are applied to the images.

   * We employ **Histogram of Oriented Gradients (HOG)** as a features extraction technique. In a nutshell, given an image (say 32x32x3) **HOG** would create a feature vector which can be consumed by a Machine Learning model. You could even experiment with simple features extraction technique such as **Edges 32x32 = 1024x1**, **Flattening the image (32x32x3) into 3072x1** and use these vectors as an input to Machine learning Models.

   

       #### Module

       * Gets license plates and non-license plate from respective directories, extract features and stores the feature vectors into the disk

            * [Sample License Plate data](https://github.com/Sardhendu/License-Plate-Detection/tree/master/DataSet/Data-Files/images_train/Licence-Plate)

            * [Sample Non-License plate data](https://github.com/Sardhendu/License-Plate-Detection/tree/master/DataSet/Data-Files/images_train/Not-Licence-Plate)

     

   ### [Train Model](https://github.com/Sardhendu/License-Plate-Detection/blob/master/Code/BldModel.py): 

   * From step 1 we already have our features, now all we have to do is send these features to a machine learning model to learn a reasonable boundary to separate License plates and Non-License plates.

   * In our case, the data is not very big, so we use **Support Vector Machines** as our machine learning model. SVM's with RBF kernel performs excellent with small data size and are robust to overfitting.

   

        ##### Module:

        * Fetches the saved features and the corresponding label (license_plate or non-license plate) and sends it to the SVM model.

        * Stores the learned model into the disk to be used while cross validation and testing. 

            * [CSV files containing the features](https://github.com/Sardhendu/License-Plate-Detection/tree/master/DataSet/Feature-Model)

             

   ### [Cross Validation](https://github.com/Sardhendu/License-Plate-Detection/blob/master/Code/LP_Detect_main.py):

   * Now that we have a model in place we would want to validate the model to predict if a rectangular region is license plate or non-license plate

   * **Note** here we do not provide the whole image, but use manually extract license plates and random images to get a sense of the model performance

   

   ### [Extract License plate](https://github.com/Sardhendu/License-Plate-Detection/blob/master/Code/LP_Detect_main.py):

   * We have every thing set up. We have a good model too and have validated it against cross validation dataset. Now the aim is to extract all the license plate given a image containing vehicles. 

   * We know that a license plate has visible edges. So, we extract all contours or region of interests **(ROI)** from a image, i.e (rectangles, circles polygon defined with intense edges).

   * We know that license plate are rectangular in shape, so we reshape/extend the **ROI** as rectangles. Note, for a given image we can have 100's or **ROI** of which only 2-3 would be license plate. So we have to classify all the **ROI** using our **SVM** model.   

   * All the **ROI's** are send to the feature extractor module.

  * The generated features for each **ROI** are then send to the **SVM** classifier for classification.

  * A high probability (say >90%) indicates a **ROI** to be the license plate. 

  * Finally we stash the **ROI** that have high probability of being a license plate into the disk for manual examination.

      

  **Look HERE** to get a sense of the directory name, so that you can run the model for yourself.

      

      

## Snapshot of the process



#### Train: 

Training data is manually created by taking a vehicle's image and cropping out the license plate image. Another 

simple way would be to extract all possible contours form the image (look at /Code/Classify.py) and save them in a 

directory. Then manually go an select the license plates. Below are some images of License plates used for Training.



##### License Plates:



  



##### Non License Plates



  



#### Test:

For testing, an image containing a vehicle image with its license plate was provided. Given the image we first 

extracted all the **ROI's** and then classify each **ROI's** as a license plate or not a license plate.



##### Test Images:

  



##### Contours Extracted (Region of Interest)  

  



##### Region of interest classified as License Plate

 



A sample of Dataset is provided: Look under the directory folder "/DataSet" to get a sense of the dataset.