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Bangladeshi License Plate Recognition: Character Recognition Dataset

4061272190
Tagsurveillance
Taskobject detection
Release YearMade in 2022
LicenseApache 2.0
Download117 MB

Introduction #

Syed Nahin Hossain, Md.Zahim Hassan, Md.Masum Al Masba

Authors introduce the Bangladeshi License Plate Recognition: Character Recognition dataset, which serves as a valuable resource for object detection within the surveillance industry. This dataset comprises 4,061 images, encompassing 35,705 labeled objects distributed across 27 distinct classes such as metro, dhaka, and numerical digits, including 1, 2, 3, 4, 5, 6, 7, 8, 9, as well as various characters and city names like khulna, ba, ja, chattogram and more. The dataset is partitioned into three subsets: train (3,480 images), validation (363 images), and test (218 images), making it a comprehensive tool for character recognition and license plate analysis in the Bangladeshi context.

About Automatic License Plate Recognition System For Bangladeshi Vehicles Using Deep Neural Network

The Bangladeshi License Plate Recognition: License Plate Localization dataset is a part of Automatic License Plate Recognition System (ALPRS) For Bangladeshi Vehicles Using Deep Neural Network work, which includes two datasets:

  • Bangladeshi License Plate Recognition: License Plate Localization (available on DatasetNinja)
  • Bangladeshi License Plate Recognition: Character Recognition (current)

The authors highlight that an ALPRS faces three main challenges as a whole. These are properly detecting a vehicle’s license plate, segmenting the texts in the license plate, andrecognizing the texts or characters or digits. Apart from these three challenges, another important challenge is to generate properly formatted output which is ready to use. Authors focuses on various ways of overcoming these challenges with the help of Deep Neural Networks and presents the most feasible solution for a complete ALPR system. Experimenting with different DNN models, authors have come up with the most effcient & robust solution in every stage. Authors have merged the segmentation phase with the recognition phase to make this process easy. By doing such, authors can save a lot of time and computational effort. Finally, authors have presented their custom algorithm, which is computationally effcient and generates a properly formatted output for their system.

image

Workflow diagram of the proposed system.

About Datasets:

One of the main contributions of authors’ work is the rich datasets for both localization and recognition of the Bangladeshi license plate. Authors’ first dataset (Bangladeshi License Plate Recognition: License Plate Localization) contains almost 2800 images for localization. The second dataset (Bangladeshi License Plate Recognition: Character Recognition) contains around 4000 license plate images cropped from the first dataset which are the most so far in this sector. Authors have split their datasets into 70:15:15 and 85:10:5 for training, validation, and testing purpose in license plate localization and text recognition stage respectively. Authors’ datasets contain images from different cities of Bangladesh: Dhaka, Khulna, Chattogram, Jashore including different vehicle categories license plates from both private and trading vehicles. Authors’ datasets are diverse enough and cover almost every possible condition, angle, and environment. To create this datasets more diverse authors have gathered images from different sources. From Nooruddin et al. authors are given their dataset of only trading vehicles. Most of the private vehicle images are used from paper Rahman et al., and the rest of them are collected by authors.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Bangladeshi License Plate Recognition: Character Recognition is a dataset for an object detection task. It is used in the surveillance industry.

The dataset consists of 4061 images with 35705 labeled objects belonging to 27 different classes including metro, dhaka, 1, and other: 2, 0, ga, 3, 4, 5, 9, 7, 8, 6, ta, khulna, ba, ja, gha, kha, jashore, chattogram, ha, ca, da, ka, va, and na.

Images in the Bangladeshi License Plate Recognition: Character Recognition dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (3480 images), validation (363 images), and test (218 images). The dataset was released in 2022 by the Khulna University of Engineering & Technology, Bangladesh.

Dataset Poster

Explore #

Bangladeshi License Plate Recognition: Character Recognition dataset has 4061 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 Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
OpenSample annotation mask from Bangladeshi License Plate Recognition: Character RecognitionSample image from Bangladeshi License Plate Recognition: Character Recognition
👀
Have a look at 4061 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
metro
rectangle
3720
3722
1
11.46%
dhaka
rectangle
3235
3239
1
8.55%
1
rectangle
3108
5304
1.71
6.76%
2
rectangle
2077
2598
1.25
5.4%
0
rectangle
2017
2861
1.42
5.4%
ga
rectangle
1859
1860
1
3.57%
3
rectangle
1851
2462
1.33
5.83%
4
rectangle
1704
2080
1.22
5.1%
5
rectangle
1695
2188
1.29
5.03%
9
rectangle
1502
1781
1.19
5%

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
1
rectangle
5304
4.01%
7.48%
1.6%
34px
14.01%
159px
52.25%
78px
32.37%
17px
4.09%
121px
29.09%
metro
rectangle
3722
11.46%
17.14%
3.34%
34px
19.02%
172px
65.48%
81px
34.4%
54px
12.98%
173px
41.59%
dhaka
rectangle
3239
8.54%
17.18%
3.49%
35px
15.85%
157px
56.76%
69px
29.73%
64px
15.38%
202px
48.56%
0
rectangle
2861
3.83%
6.88%
1.63%
37px
13.55%
147px
60.71%
80px
32.16%
23px
5.53%
103px
24.76%
2
rectangle
2598
4.34%
7.4%
1.99%
43px
17.59%
152px
54.64%
80px
34.09%
25px
6.01%
85px
20.43%
3
rectangle
2462
4.4%
7.17%
2.06%
34px
16.15%
140px
55.64%
78px
33.6%
23px
5.53%
80px
19.23%
5
rectangle
2188
3.91%
6.2%
1.81%
37px
14.79%
146px
67.86%
78px
33.41%
22px
5.29%
66px
15.87%
4
rectangle
2080
4.19%
7.01%
1.83%
37px
14.93%
153px
50%
80px
33.75%
25px
6.01%
126px
30.29%
ga
rectangle
1860
3.57%
8.67%
2.07%
33px
16.13%
123px
37.87%
65px
28.76%
34px
8.17%
125px
30.05%
9
rectangle
1781
4.23%
6.98%
1.85%
44px
15.62%
166px
47.57%
81px
33.28%
25px
6.01%
78px
18.75%

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 35705 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 35705
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
dhaka
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
60px
28.04%
117px
28.12%
7.89%
2
metro
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
68px
31.78%
134px
32.21%
10.24%
3
ga
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
55px
25.7%
48px
11.54%
2.97%
4
3
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
58px
27.1%
54px
12.98%
3.52%
5
1
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
61px
28.5%
51px
12.26%
3.49%
6
1
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
61px
28.5%
51px
12.26%
3.49%
7
2
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
61px
28.5%
53px
12.74%
3.63%
8
0
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
62px
28.97%
51px
12.26%
3.55%
9
4
rectangle
car-6285_68_1_jpg.rf.a94caafa68440fed951d8fa27adf826f.jpg
214 x 416
61px
28.5%
55px
13.22%
3.77%
10
khulna
rectangle
IMG_20190413_123737_677_1_jpg.rf.ae35db2d6dfa38520981b5db8070e0db.jpg
216 x 416
65px
30.09%
127px
30.53%
9.19%

License #

Bangladeshi License Plate Recognition: Character Recognition is under Apache 2.0 license.

Source

Citation #

If you make use of the BLPR: Character Recognition data, please cite the following reference:

@InProceedings{10.1007/978-981-16-6636-0_8,
  author="Hossain, Syed Nahin and Hassan, Md. Zahim and Masba, Md. Masum Al",
  editor="Arefin, Mohammad Shamsul and Kaiser, M. Shamim and Bandyopadhyay, Anirban and Ahad, Md. Atiqur Rahman and Ray, Kanad",
  title="Automatic License Plate Recognition System for Bangladeshi Vehicles Using Deep Neural Network",
  booktitle="Proceedings of the International Conference on Big Data, IoT, and Machine Learning",
  year="2022",
  publisher="Springer Singapore",
  address="Singapore",
  pages="91--102",
  isbn="978-981-16-6636-0"
}

Source

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

@misc{ visualization-tools-for-bangladeshi-license-plate-recognition-character-dataset,
  title = { Visualization Tools for Bangladeshi License Plate Recognition: Character Recognition Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/bangladeshi-license-plate-recognition-character } },
  url = { https://datasetninja.com/bangladeshi-license-plate-recognition-character },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset Bangladeshi License Plate Recognition: Character Recognition can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Bangladeshi License Plate Recognition: Character Recognition', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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