Dataset Ninja LogoDataset Ninja:

EALPR: Plates Dataset

2068271309
Tagsurveillance
Taskobject detection
Release YearMade in 2021
Licenseunknown

Introduction #

Released 2021-12-09 ·Ahmed Ramadan Youssef, Fawzya Ramadan Sayed, Abdelmgeid Ameen Ali

EALPR: Egyptian Automatic License Plate Recognition (Plates) is a comprehensive dataset tailored for object detection tasks, particularly focused on the recognition and localization of diverse characters found on license plates. Comprising 2068 images, the dataset encompasses a substantial 10797 annotated objects, spread across 27 distinct classes, including characters like ١, م, أ, among others such as ٦, س, ر, and a variety of Arabic characters. The dataset showcases the versatility of characters present on license plates, especially exemplified by the Egyptian License Plate (LP), which typically incorporates Arabic digits and characters. This rich collection captures the variation in characters utilized on plates from different regions, including those that use Latin letters, such as Europe, Brazil, and the US, thereby serving as a valuable resource for training models to recognize and interpret diverse international license plate characters.

EALPR Structure

image

EALPR: Vehicles demo.

image

EALPR: Plates demo.

Proposed method

Authors use two object detection models for license plate detection and recognition. The input to authors’ proposed system is the vehicle image, whereas the output is the recognized license plate characters and digits. Tiny-YOLOV3 is the backbone architecture for both stages LP detection and recognition.

image

Main Stages of the Proposed EALPR Approach.

About EALPR

Authors scrap the EALPR dataset images from websites such as Instagram pages and Facebook Marketplace using web scraping Tools. It has 2,450, and 12,160 characters images including many types of vehicles such as cars, buses, trucks, microbuses, and mini-busses. The vehicle images are captured using different cameras, at varying times, lighting, and background.

image

Characters and Digits Statistics in the EALPR Dataset.

The Egyptian License Plate (LP) uses the Arabic digits and characters which is varying in several countries that use Latin letters such as Europe, Brazil, US, … etc. It has a dimension 16 (height) X 40 (width) with aspect ratio 1:2 approximately. its layout is divided into 3 regions: the country region contains the Egypt word in Arabic and English format, the character region includes plate characters, and the number region contains plate digits. Egyptian License Plate structure uses 10 Arabic digits and 17 Arabic characters.

image

Egyptian License Plate Structure.

image

Plate Arabic Digits and Latin Digits.

image

Plate Arabic Characters and Latin Characters.

After collecting EALPR dataset authors manually annotate the vehicles, characters, and digits using Ybat tool.

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Dataset LinkHomepageDataset LinkResearch Paper 1 (main)Dataset LinkResearch Paper 2

Summary #

EALPR: Egyptian Automatic License Plate Recognition (Plates) is a dataset for an object detection task. It is used in the surveillance industry.

The dataset consists of 2068 images with 10797 labeled objects belonging to 27 different classes including ١, م, أ, and other: ٦, س, ر, ٥, د, ٤, ع, ٧, ھ, ٢, ن, و, ٣, ل, ٩, ج, ب, ى, ٨, ق, ط, ف, ص, and ٠.

Images in the EALPR: Plates dataset have bounding box annotations. There are 90 (4% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2021 by the Fayoum University, Egypt and Minia University, Egypt.

Dataset Poster

Explore #

EALPR: Plates dataset has 2068 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
OpenSample annotation mask from EALPR: PlatesSample image from EALPR: Plates
👀
Have a look at 2068 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 27 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 27
Class
Images
Objects
Count on image
average
Area on image
average
١
rectangle
769
1408
1.83
4.36%
م
rectangle
471
616
1.31
4.22%
أ
rectangle
397
465
1.17
2.8%
٦
rectangle
378
1058
2.8
9.52%
س
rectangle
359
434
1.21
4.15%
ر
rectangle
348
383
1.1
3.33%
٥
rectangle
310
644
2.08
5.03%
د
rectangle
296
358
1.21
2.86%
٤
rectangle
291
615
2.11
7.19%
ع
rectangle
287
323
1.13
5.21%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 27
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
١
rectangle
1408
2.38%
5%
1.11%
11px
23.17%
162px
51.97%
34px
35.33%
4px
4.07%
74px
13.48%
٦
rectangle
1058
3.43%
5.59%
1.95%
11px
25.45%
108px
47.14%
33px
35.02%
6px
6.57%
54px
13.35%
٥
rectangle
644
2.44%
4.12%
1.31%
9px
17.39%
64px
36.08%
25px
25.53%
5px
6.1%
49px
14.46%
م
rectangle
616
3.22%
5.34%
1.96%
11px
21.95%
156px
44.02%
34px
33.33%
6px
6.4%
93px
14%
٤
rectangle
615
3.42%
5.45%
2.01%
13px
26.09%
152px
52.36%
38px
35.53%
7px
6.98%
76px
13.11%
٧
rectangle
550
3.53%
7.47%
1.95%
10px
20.73%
187px
48.18%
34px
33.63%
5px
6.19%
96px
15.89%
أ
rectangle
465
2.39%
4.77%
1.35%
14px
25.45%
140px
50.19%
36px
36.03%
5px
3.66%
54px
12.4%
٢
rectangle
435
3.34%
6.48%
1.92%
11px
21.25%
156px
48.86%
34px
35.05%
7px
6.86%
73px
16.95%
س
rectangle
434
3.44%
6.02%
2.01%
8px
17.07%
88px
41.89%
24px
24.67%
9px
11.39%
81px
19.48%
٩
rectangle
403
3.37%
5.8%
1.82%
11px
26.67%
147px
51.18%
37px
35.24%
7px
6.19%
71px
13.25%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 10797 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 10797
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
٩
rectangle
1087_license_plate_1.png
95 x 164
31px
32.63%
13px
7.93%
2.59%
2
م
rectangle
1087_license_plate_1.png
95 x 164
33px
34.74%
15px
9.15%
3.18%
3
م
rectangle
1087_license_plate_1.png
95 x 164
31px
32.63%
13px
7.93%
2.59%
4
١
rectangle
1571_license_plate_1.png
75 x 131
27px
36%
11px
8.4%
3.02%
5
١
rectangle
1571_license_plate_1.png
75 x 131
25px
33.33%
13px
9.92%
3.31%
6
١
rectangle
1571_license_plate_1.png
75 x 131
27px
36%
11px
8.4%
3.02%
7
١
rectangle
1571_license_plate_1.png
75 x 131
29px
38.67%
11px
8.4%
3.25%
8
م
rectangle
1571_license_plate_1.png
75 x 131
29px
38.67%
13px
9.92%
3.84%
9
ر
rectangle
1571_license_plate_1.png
75 x 131
21px
28%
17px
12.98%
3.63%
10
أ
rectangle
1571_license_plate_1.png
75 x 131
28px
37.33%
11px
8.4%
3.13%

License #

License is unknown for the Egyptian Automatic License Plate Recognition: Plates dataset.

Source

Citation #

If you make use of the EALPR: Plates data, please cite the following reference:

@INPROCEEDINGS{9845514,
  author={Youssef, Ahmed Ramadan and Sayed, Fawzya Ramadan and Ali, Abdelmgeid Ameen},
  booktitle={2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)}, 
  title={A New Benchmark Dataset for Egyptian License Plate Detection and Recognition}, 
  year={2022},
  volume={},
  number={},
  pages={106-111},
  doi={10.1109/ACIRS55390.2022.9845514}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-ealpr-plates-dataset,
  title = { Visualization Tools for EALPR: Plates Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/ealpr-plates } },
  url = { https://datasetninja.com/ealpr-plates },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Please visit dataset homepage to download the data.

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