Arabic Car License Recognition. A solution to the kaggle competition Machathon 3.0.

Last update: Dec 04, 2022

Overview

Transformers

Arabic licence plate recognition 🚗

Solution to the kaggle competition Machathon 3.0.
Ranked in the top 6️⃣ at the final evaluation phase.
Check our solution now on collab!
Check the solution presentation

Preprocessing Pipeline

Approach

Step1: Preprocessing Enhancments on the image.

Most images had bad illumination and noise
- Morphological operations to Maximize Contrast.
- Gaussian Blur to remove Noise.
Thresholding on both Value and Saturation channels.

Step2: Extracting white plate using countours.

Get countours and sort based on Area.
Polygon Approximation For noisy countours.
Convex hull for Concave polygons.
4-Point transformation For difficult camera angles.

Now have numbers in a countor and letters in another.

Step3: Separating characters from white plate using sliding windows.

Can't use countours to get symbols in white plate since Arabic Letter may consist of multiple charachters e.g ت this may consist of 2/3 countours.

Solution

Tuned 2 sliding windows, one for letters' white plate, the other for numbers.
- Variable window width
- Window height is the white plate height, since arabic characters may consist multiple parts
Selecting which window
- Must have no black pixels on the sides
- Must have a specific range of black pixels inside
- For each group of windows the one with max black pixels is selected

Step4: Character Recognition.

Training 2 model since Arabic letters and numbers are similar e.g (أ,1) (5, ه)
- one for classifing only arabic letters.
- one for classifying arabic numbers.

Project Organization

Scripts applied on images

./Macathon/code/
├── extract_bbx_xml.ipynb                       : Takes directory of images and their bbx data stored in an xml files, and crop the bbxs from the images.
|                                                 The xml file contains licence label(name), xmin, ymin, xmax, ymax of the bbxs in an image.    
├── extract_bbx_txt.ipynb                       : Takes directory of images and their bbx data stored in a txt files, and crop the bbxs from the images.
|                                                 The txt file corresponding to one image may consist of multiple bbxs, each corresponds to a row of xmin,ymin,xmax,ymax for that bbx.
└── crop_right_noise.ipynb                      : Crops an image with some percentage and replace with the cropped image.

Model versions

./Macathon/code/
└── model.ipynb                      : - The preprocessing and modeling stage, Contains:
                                          - Preprocessing Functions
                                          - Training both classifers
                                          - Prediction and generating the output csv file

Data Folder

./Macathon/data/
├── challenging_images.rar                      : Contains most challenging images collected from the train data. 
├── cropped_letters.zip                         : 28 Subfolders corresponding to the 28 letter in Arabic alphabet.
|                                                 Each subfolder holds images for the letter it's named after, cropped from the train data distribution.
├── cropped_numbers.zip                         : 10 Subfolders for the 10 numbers.
|                                                 Each subfolder holds images for the number it's named after, cropped from the train data distribution.
├── machathon-3.zip                             : The uploaded data found with the kaggle competition.
└── testLetters.zip                             : 200 images labeled from the test data distribution.
                                                  Each image has a corresponding xml file holding the bbxs locations in it.

Contributors

This masterpiece was designed, and implemented by

_{Hossam Saeed}

_{Mostafa Wael}

_{Nada Elmasry}

_{Noran Hany}

Arabic Car License Recognition. A solution to the kaggle competition Machathon 3.0.

Related tags

Overview

Transformers

Arabic licence plate recognition 🚗

Preprocessing Pipeline

Approach

Step1: Preprocessing Enhancments on the image.

Step2: Extracting white plate using countours.

Step3: Separating characters from white plate using sliding windows.

Solution

Step4: Character Recognition.

Project Organization

Scripts applied on images

Model versions

Data Folder

Contributors

This masterpiece was designed, and implemented by

Owner

Noran Hany

Playable Video Generation

Implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features"

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

Supercharging Imbalanced Data Learning WithCausal Representation Transfer

Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

MINOS: Multimodal Indoor Simulator

Python implementation of Lightning-rod Agent, the Stack4Things board-side probe

Clean and readable code for Decision Transformer: Reinforcement Learning via Sequence Modeling

A PyTorch implementation: "LASAFT-Net-v2: Listen, Attend and Separate by Attentively aggregating Frequency Transformation"

Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"

Post-Training Quantization for Vision transformers.

Official Implementation of LARGE: Latent-Based Regression through GAN Semantics

Deep motion transfer

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

TransPrompt - Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification

A Fast and Accurate One-Stage Approach to Visual Grounding, ICCV 2019 (Oral)

Official Pytorch implementation for AAAI2021 paper (RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning)

Simple image captioning model - CLIP prefix captioning.

An essential implementation of BYOL in PyTorch + PyTorch Lightning