Dataset Ninja LogoDataset Ninja:

ASAYAR Dataset

3698131131
Tagself-driving
Taskobject detection
Release YearMade in 2021
Licensecustom

Introduction #

Mohammed Akallouch, Kaoutar Sefrioui Boujemaa, Afaf Bouhouteet al.

The creators of the ASAYAR: A Dataset for Arabic-Latin Text Detection gathered 1765 images along Moroccan highways, encompassing two complete journeys totaling around 2000 km. These trips consist of the first route from Oujda to Rabat and the second from Tangier to Casablanca. The images were captured using a Nikon D3300 camera with 24 megapixels and a Samsung Galaxy S7 smartphone camera with 13 megapixels. They encompass a variety of Moroccan highway signs, including those with Arabic/French text, regulatory indications, and warning traffic signs. The data annotation process took into account environmental factors.

The labelers of ASAYAR invested over 2000 hours of work, spanning nine months, to manually annotate the dataset. The annotated detection dataset is organized into one of three type groups: ASAYAR_SIGN for traffic sign objects, ASAYAR_TXT for text-based panels with word and line level annotations, and ASAYAR_SYM for panels featuring annotated directional symbols.

The authors employed the stratified sampling method to divide the sub-datasets into training (80%) and testing (20%) sets, ensuring balanced classes in each. For ASAYAR_SIGN, the training set comprises 1416 images, with 349 in the testing set. ASAYAR_TXT includes 1100 training samples and 275 testing samples, incorporating both word-level and line-level datasets. Lastly, ASAYAR_SYM has 444 images for training and 114 for testing.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

ASAYAR: A Dataset for Arabic-Latin Text Detection is a dataset for an object detection task. It is used in the automotive industry, and in the optical character recognition (OCR) domain.

The dataset consists of 3698 images with 14382 labeled objects belonging to 13 different classes including guide sign, arabic box, latin box, and other: number, regulatory sign, down right arrow, down arrow, mixed arrow, warning sign, right arrow, up arrow, down left arrow, and mixed box.

Images in the ASAYAR dataset have bounding box annotations. There is 1 unlabeled image (i.e. without annotations). There are 2 splits in the dataset: train (2960 images) and test (738 images). Addirionally, every image contains information about its type: sign, text, or sym. The dataset was released in 2021 by the Sidi Mohamed Ben Abdellah University, Morocco and Mohammed VI Polytechnic University, Morocco.

Here are the visualized examples for the classes:

Explore #

ASAYAR dataset has 3698 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 ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
OpenSample annotation mask from ASAYARSample image from ASAYAR
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Have a look at 3698 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 13 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 13
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
guide signβž”
rectangle
1557
1899
1.22
3.51%
arabic boxβž”
rectangle
1142
4467
3.91
0.84%
latin boxβž”
rectangle
1096
4810
4.39
0.51%
numberβž”
rectangle
1061
1630
1.54
0.12%
regulatory signβž”
rectangle
409
475
1.16
0.63%
down right arrowβž”
rectangle
340
390
1.15
0.13%
down arrowβž”
rectangle
158
353
2.23
0.13%
mixed arrowβž”
rectangle
127
127
1
0.64%
warning signβž”
rectangle
77
82
1.06
0.42%
right arrowβž”
rectangle
61
61
1
0.41%

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 12
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
latin box
rectangle
4810
0.12%
2.54%
0%
3px
0.28%
109px
10%
28px
2.54%
2px
0.1%
487px
25.36%
arabic box
rectangle
4467
0.22%
2.56%
0%
2px
0.19%
150px
13.76%
41px
3.79%
3px
0.16%
438px
22.81%
guide sign
rectangle
1899
2.88%
22.7%
0.01%
11px
1.01%
777px
71.28%
192px
17.7%
15px
0.78%
807px
42.03%
number
rectangle
1630
0.08%
0.65%
0.01%
9px
0.83%
101px
9.35%
30px
2.76%
9px
0.47%
148px
7.71%
regulatory sign
rectangle
475
0.54%
4.27%
0.01%
11px
1.01%
316px
29.26%
90px
8.35%
11px
0.57%
364px
18.96%
down right arrow
rectangle
390
0.12%
8.03%
0.01%
10px
0.93%
391px
35.87%
42px
3.88%
11px
0.57%
430px
22.4%
down arrow
rectangle
353
0.06%
0.42%
0.01%
23px
2.11%
100px
9.17%
50px
4.61%
8px
0.42%
96px
5%
mixed arrow
rectangle
127
0.64%
2.9%
0.05%
18px
1.67%
502px
46.06%
185px
17.02%
22px
1.15%
187px
9.74%
warning sign
rectangle
82
0.4%
6.48%
0.01%
12px
1.11%
349px
32.31%
67px
6.18%
14px
0.73%
385px
20.05%
up arrow
rectangle
77
0.09%
0.92%
0.01%
16px
1.48%
501px
45.96%
69px
6.37%
9px
0.47%
56px
2.92%

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 14382 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 14382
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
arabic box
rectangle
txt_image_1149.png
1090 x 1920
43px
3.94%
71px
3.7%
0.15%
2βž”
arabic box
rectangle
txt_image_1149.png
1090 x 1920
40px
3.67%
132px
6.88%
0.25%
3βž”
arabic box
rectangle
txt_image_1149.png
1090 x 1920
44px
4.04%
114px
5.94%
0.24%
4βž”
arabic box
rectangle
txt_image_1149.png
1090 x 1920
44px
4.04%
109px
5.68%
0.23%
5βž”
latin box
rectangle
txt_image_1149.png
1090 x 1920
30px
2.75%
49px
2.55%
0.07%
6βž”
latin box
rectangle
txt_image_1149.png
1090 x 1920
34px
3.12%
136px
7.08%
0.22%
7βž”
latin box
rectangle
txt_image_1149.png
1090 x 1920
31px
2.84%
30px
1.56%
0.04%
8βž”
latin box
rectangle
txt_image_1149.png
1090 x 1920
29px
2.66%
105px
5.47%
0.15%
9βž”
latin box
rectangle
txt_image_1149.png
1090 x 1920
31px
2.84%
30px
1.56%
0.04%
10βž”
latin box
rectangle
txt_image_1149.png
1090 x 1920
32px
2.94%
149px
7.76%
0.23%

License #

This dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching or scientific publications. Permission is granted to use the data given that you agree:

  1. That the dataset comes β€œAS IS”, without express or implied warranty. Although every effort has been made to ensure accuracy, we (vcar) do not accept any responsibility for errors or omissions
  2. That you include a reference to the ASAYAR Dataset in any work that makes use of the dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the ASAYAR website.
  3. That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
  4. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
  5. That all rights not expressly granted to you are reserved by us.

Source

Citation #

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

@ARTICLE{9233923,
  author={M. {Akallouch} and K. S. {Boujemaa} and A. {Bouhoute} and K. {Fardousse} and I. {Berrada}},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={ASAYAR: A Dataset for Arabic-Latin Scene Text Localization in Highway Traffic Panels}, 
  year={2020},
  pages={1-11},
  doi={10.1109/TITS.2020.3029451}
} 

Source

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

@misc{ visualization-tools-for-asayar-dataset,
  title = { Visualization Tools for ASAYAR Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/asayar } },
  url = { https://datasetninja.com/asayar },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Please visit dataset homepage to download the data.

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