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

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"

Fast and simple implementation of RL algorithms, designed to run fully on GPU.

Expressive Power of Invariant and Equivaraint Graph Neural Networks (ICLR 2021)

Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF)

Unofficial PyTorch implementation of Guided Dropout

[AAAI2021] The source code for our paper 《Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion》.

Code for our paper 'Generalized Category Discovery'

PyTorch implementation of Asymmetric Siamese (https://arxiv.org/abs/2204.00613)

The authors' implementation of Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations

SeqTR: A Simple yet Universal Network for Visual Grounding

Simple tools for logging and visualizing, loading and training

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (Findings of EMNLP 2021))

Telegram chatbot created with deep learning model (LSTM) and telebot library.

Supervised Contrastive Learning for Product Matching

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

GraphGT: Machine Learning Datasets for Graph Generation and Transformation

Extending JAX with custom C++ and CUDA code

CellRank's reproducibility repository.

Pytorch implementation of ICASSP 2022 paper Attention Probe: Vision Transformer Distillation in the Wild