• License Plate Detection dataset images and annotations were cloned from the following projects:
  • https://universe.roboflow.com/carplates2/licenseplate_v2
  • https://universe.roboflow.com/a-stx8a/licenseplateimage_thresholdfiltered---roboflow/
  • https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk
  • https://universe.roboflow.com/xavier-jimenez/placas-stcpz/
  • https://universe.roboflow.com/yolo-training-jaqog/yolo-training-wis72/
  • https://universe.roboflow.com/abdullahi-ayantayo/car-license-plate-eosye/

This is a U.S. license plate dataset + model using object detection. The images for this dataset were collected from Google images and around Central Florida parks. If you see your license plate in this dataset and you wish to remove it, please contact friends@roboflow.com

Try it out on this example web app or deploy to Luxonis Oak .

This project was completed by Alfonso Blanco Garcia.

The project description and implementation is below:

From some files of images and labels obtained by applying the project presented at https://github.com/ashok426/Vehicle-number-plate-recognition-YOLOv5, the images of license plates are filtered through a threshold that allows a better recognition of the license plate numbers by pytesseract.

Requirements:

The tests have been carried out on a computer with Windows11

Must installed the packages corresponding to :

import pytesseract

import numpy as np

import cv2

import re

import os

It is convenient to have Spyder installed for the execution of the program and Anaconda for the installation of the aforementioned packages from the cmd.exe option

Functioning:

Once the files have been downloaded, the test1 folder wihch contains the images and labels to test, is decompressed with its subfolders:

images (which contains the car images, (downloaded from https://roboflow.com/) and the labels obtained by applying yolov5 to those images.
labels: which contains the coordinates of the boxes demarcated by yolov5 for the car and license plate objects. ( have been obtained by applying https://github.com/ashok426/Vehicle-number-plate-recognition-YOLOv5)
The test1 folder should be in the same directory as the GetNumberLicencePlate.py program.

Run:

GetNumberLicencePlate.py

For inspection, it presents the images of each car and then those of its license plate, you have to close them for the program to continue advancing. Sometimes the image stays on the taskbar, you have to click on it and close it.

By console the following messages are obtained:

Reading images from test1\images\

Reading labels from test1\labels\

Number of images : 7

Number of labels : 7

Car 1 the license plate is not recognized

Car 2 the license plate is not recognized

Car 3 the license plate is recognized : 2122267

Car 4 the license plate is recognized : VI47 JAR

Car 5 the license plate is recognized : WM64 UMA

Car 6 the license plate is recognized : PL GO 321

Car 7 the license plate is recognized : M5 CEU

Comments:

Car1 has excessive light exposure on the license plate, so the license plate image appears burnt.

Car 2 has a slanted license plate so it is not recognized by pytesseract

Car 6's registration is recognized, but it has the error that it has assigned the L that appears, to an I that appears in the real registration.

The chosen filtering method is the threshold method, after seeing in the article https://aicha-fatrah.medium.com/improve-the-quality-of-your-ocr-information-extraction-ebc93d905ac4 the radical improvement by applying this filtering to the OCR recognition of a French national identity document.

However, this threshold level cannot be fixed for each image, so one is implemented based on the total brightness of the image (sum of the values of each pixel) to which an adjustment factor and bias are applied. Although the result is not applicable to all images due to their different qualities and circumstances.

For license plate recognition it is necessary to filter the various license plate number formats in which they are presented. In the formats of license plates from roboflow images, a great variety and anarchy of formats is detected.

As the program operates on label files created with yolov5, it is convenient to test by installing the application https://github.com/ashok426/Vehicle-number-plate-recognition-YOLOv5, however when doing it on a windows11 computer I have had I have to make some changes and I have made some simplifications that may be of interest to someone who works in that environment and which I attach to this document:

On 05/23/2022 a new version is introduced:

GetNumberLicensePlateV1.py

Which is intended by varying the threshold level in a loop above and below the experimental formula of the thresold, that the correct enrollment is obtained for the most part.

For its execution, it needs that in the same directory of the program there is the Test2 directory that is attached and that contains the images and labels of the test.

It also requires the TrueLicenses.txt file in which the true license plates go, arranged in the same order as the images, to allow verification.

The results are:

HIT: the true license plate has been detected as the majority among all those provided by pytesseract

DETECTED: The true license plate has been detected by pytesseract, but it is not the majority.

NOT DETECTED: pytesseract has detected license plates, but none match the real one

NOT RECOGNIZED: pytesseract could not detect a license plate number.

The pyteseract results are filtered according to some registration formats, not all, which allows debugging.

This is a very slow brute force process

On 07/04/2022 a new ML version is introduced:

From a set of images of cars with the labels of their license plates (test4Training folder), a Y_train is formed consisting of the set of thresholds at which each license plate is recognized by pytesseract, the X_train is made up of all the pixels of each image and the engine it is the sklearn SVM that allows you to make predictions of the threshord that would require a photo license plate to be recognized by pytesseract.

The training file is created by running: CreateThresoldTraining.py

the Y_train values are in the files, one by each license, en test4Training\thresholds

Executing:

GetNumberLicencePlateV6SVM.py

The predictions of first ten licenses plate of test4Training are getting

Observations:

Only images for which pytesseract can recognize the license plate number by varying the threshold are considered.

The error rate is not good since it operates with a training of 114 thresholds, which is small. When executing GetNumberLicencePlateV6SVM,py for the first 10 license plates, license plate 1032148 appears confused as 1932148, however adjusting the parameters at the beginning of the program: imgStart=1 imgEnd=3 With which the test is reduced to a single record and the training is increased by 9 records, the licenset appears correctly predicted.

The name of each image, label and thresold has been changed. originally assigned by roboflow, so that they correspond to the license plate number, allowing to detect failures and successes in the tests

On 08/16/2022

A new version is introduced that makes use of the filtering provided by the OTSU filter (developed at https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html and https://learnopencv.com/otsu-thresholding-with-opencv/) which is complemented in its failures by two filters , one based on the brightness of the image and another on the stability of the image with the variations of the threshold (similar as in cv2.MSER). Using all the possibilities of the THRESH parameter

It is tested by running the python module:

GetLisencePlateSeveralThresholds.py

that only need the test4Training folder with 114 image files and their labels.

the result is :

Reading labels from test4Training\labels
Reading imagenes from test4Training\images
Image of GE1466 with low standard deviation, will be difficult to be recognized
Image of LCA850 with low standard deviation, will be difficult to be recognized
Image of SHEILL with low standard deviation, will be difficult to be recognized

Number of imagenes : 114
Number of labels : 114
Number of licenses : 114

1000051 Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

1032148 Hit with adaptive Threshold Mean and THRESH_BINARY

1128NS Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

118GRP Hit with adaptive Threshold Mean and THRESH_BINARY

1277DPZ Hit with Brightness and THRESH_TOZERO

2122267 Hit with adaptive Threshold Mean and THRESH_BINARY

247VIP9 Hit with Brightness and THRESH_BINARY

25810X Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

2999961 Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

3008GWK Hit with OTSU and THRESH_TRUNC

3100000 Hit with OTSU and THRESH_TOZERO

41RSTZ Hit with Brightness and THRESH_BINARY

5752FXX Hit with adaptive Threshold Gaussian and THRESH_BINARY

5777770 Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

589222 Hit with OTSU and THRESH_BINARY

9222228 Hit with Otsu's thresholding of cv2 , Gaussian filtering and THRESH_BINARY

9999868 Hit with adaptive Threshold Gaussian and THRESH_BINARY

A69985T Hit with OTSU and THRESH_TRUNC

ACHTUNG Hit with adaptive Threshold Mean and THRESH_BINARY

AHYEW Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

ARZT120 Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

AUDIZINE Hit with adaptive Threshold Mean and THRESH_BINARY

B76ZHN Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

B81JKY Hit with Otsu's thresholding of cv2 , Gaussian filtering and THRESH_BINARY

BANY976 Hit with adaptive Threshold Mean and THRESH_BINARY

BEHM927 Hit with adaptive Threshold Mean and THRESH_BINARY

BM351 Hit with Brightness and THRESH_TRUNC

BMWM5 Hit with OTSU and THRESH_TOZERO

BT63UYN Hit with OTSU and THRESH_TRUNC

CBMUK Hit with adaptive Threshold Mean and THRESH_BINARY

EMOTION Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

EXCUSEM3 Hit with OTSU and THRESH_TOZERO_INV

FVIOUHP Hit with Otsu's thresholding of cv2 , Gaussian filtering and THRESH_BINARY

GN64OTP Hit with Otsu's thresholding of cv2 , Gaussian filtering and THRESH_BINARY

GOOGLE Hit with Brightness and THRESH_TRUNC

GPG Hit with adaptive Threshold Mean and THRESH_BINARY

HAMSTUR Hit with adaptive Threshold Mean and THRESH_BINARY

HF3461 Hit with adaptive Threshold Mean and THRESH_BINARY

HF58HXP Hit with adaptive Threshold Mean and THRESH_BINARY

HTTPME Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

INA7506 Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

J1S1 Hit with OTSU and THRESH_TRUNC

LIMES Hit with OTSU and THRESH_TOZERO_INV

LNXGEEK Hit with adaptive Threshold Mean and THRESH_BINARY

M5CEU Hit with adaptive Threshold Gaussian and THRESH_BINARY

MMX3001 Hit with OTSU and THRESH_TRUNC

NTP56IM Hit with OTSU and THRESH_TRUNC

OLDHAG Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

OUTSTDN Hit with adaptive Threshold Mean and THRESH_BINARY

PLUGOK Hit with adaptive Threshold Mean and THRESH_BINARY

PWF666 Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

RDHE4D Hit with Otsu's thresholding of cv2 , Gaussian filtering and THRESH_BINARY

RLO4XVM Hit with adaptive Threshold Mean and THRESH_BINARY

SAA6878Y Hit with Otsu's thresholding of cv2 , Gaussian filtering and THRESH_BINARY

SHEILL Hit with Brightness and THRESH_TOZERO_INV

SKA Hit with OTSU and THRESH_TOZERO_INV

TL03GOG Hit with OTSU and THRESH_TRUNC

TY27924 Hit with OTSU and THRESH_TRUNC

UM96201 Hit with adaptive Threshold Mean and THRESH_BINARY

UW3932 Hit with adaptive Threshold Mean and THRESH_BINARY

V328FNN Hit with Brightness and THRESH_TRUNC

V8PTRL Hit with Stable y THRESH_BINARY_INV

VI47JAR Hit with adaptive Threshold Gaussian and THRESH_BINARY

VNK296GP Hit with OTSU and THRESH_TOZERO

VY90540 Hit with adaptive Threshold Mean and THRESH_BINARY

W326AVE Hit with Otsu's thresholding of cv2 , Gaussian filtering and THRESH_BINARY

WEFLYN Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

WM64UMA Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

WONELLY Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

WRP Hit with adaptive Threshold Mean and THRESH_BINARY

WXD1620 Hit with Stable y THRESH_TOZERO

XFG2121 Hit with Otsu's thresholding of cv2 , Gaussian filtering and THRESH_BINARY

YL08WUE Hit with Otsu's thresholding of cv2 and THRESH_BINARY_INV

ZUM619 Hit with OTSU and THRESH_TOZERO

Total Hits = 74

Total Failures = 40

The predictions have been possible because the car license plate number was known in advance, otherwise it would have been necessary to know the format of the license plate and/or to incorporate a control digit in the license plate to verify the accuracy of the number detected and, with what, many errors could even be autocorrected

References:

https://aicha-fatrah.medium.com/improve-the-quality-of-your-ocr-information-extraction-ebc93d905ac4

https://roboflow.com/

https://github.com/ashok426/Vehicle-number-plate-recognition-YOLOv5

https://gist.github.com/endolith/334196bac1cac45a4893#

https://learnopencv.com/otsu-thresholding-with-opencv/

https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html

https://omes-va.com/simple-thresholding/

https://towardsdatascience.com/the-practical-guide-for-object-detection-with-yolov5-algorithm-74c04aac4843

ultralytics/yolov5#2293

https://towardsdatascience.com/extract-text-from-memes-with-python-opencv-tesseract-ocr-63c2ccd72b69

Overview

The License Plates dataset is a object detection dataset of different vehicles (i.e. cars, vans, etc.) and their respective license plate. Annotations also include examples of "vehicle" and "license-plate". This dataset has a train/validation/test split of 245/70/35 respectively.

Use Cases

This dataset could be used to create a vehicle and license plate detection object detection model. Roboflow provides a great guide on creating a license plate and vehicle object detection model.

Using this Dataset

This dataset is a subset of the Open Images Dataset. The annotations are licensed by Google LLC under CC BY 4.0 license. Some annotations have been combined or removed using Roboflow's annotation management tools to better align the annotations with the purpose of the dataset. The images have a CC BY 2.0 license.

About Roboflow

Roboflow creates tools that make computer vision easy to use for any developer, even if you're not a machine learning expert. You can use it to organize, label, inspect, convert, and export your image datasets. And even to train and deploy computer vision models with no code required.

Overview

The Numberplate Dataset is a collection of Licence Plates that can easily be used for Automatic Number Plate Detection.

Example Footage!

Training and Deployment

The Number Plate model has been trained in Roboflow, and available for inference on the Dataset tab.
One could also build a Automatic Number Plate Recognition [ANPR] App using YOLOR and EasyOCR. This is achieved using the Roboflow Platform which you can deploy the model for robust and real-time ANPR.

About Augmented Startups

We are at the forefront of Artificial Intelligence in computer vision. With over 92k subscribers on YouTube, we embark on fun and innovative projects in this field and create videos and courses so that everyone can be an expert in this field. Our vision is to create a world full of inventors that can turn their dreams into reality

Как начать разметку?

1. Зайти в раздел "Annotate"
2. Выбрать блок "For Markup"
3. В верхнем левом углу нажать кнопку "Assign images"
4. Выбрать количество изображений, которые необходимо будет разметить (лучше размечать небольшими пачками, например, по 50 штук)
5. Нажать кнопку "Assign images"
6. Выбрать изображение для разметки и произвести разметку
7. Повторить шаг 6 для каждого изображения
8. Больше ничего нажимать не надо - для разметки новой пачки изображений повторить все с шага 1

P.S. Первые 5 шагов можно пропустить, если уже есть назначенный набор данных на ваш аккаунт

Важные моменты при разметки автомобильных номеров

• Выделять букву/цифру необходимо как можно точнее (максимум пару пикселей можно отступить)
• На большинстве номеров есть отметки KZ, RUS (и тп) - их необходимо выделять вместе с флагом (при наличии). Примеры есть в "Example batch"