Captcha Object Detection

Introduction #

Youtham Joseph

Captcha Object Detection is a dataset for an object detection task and it consists of 100000 images with 650000 labeled objects belonging to 54 different classes including numbers (1, 2, 3 … etc.) and letters: (M, d, Y, R, 6, C, f, s, a, J, y, 2, t, Q, G, K, E, g, k, c, z, 9, p, P, S, and more). Dataset made for defining captcha elements. It is generated using a forked version of the Captcha library by lepture.

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Summary #

Captcha Object Detection is a dataset for an object detection task. Possible applications of the dataset could be in the optical character recognition (OCR) domain and security industry.

The dataset consists of 100000 images with 650000 labeled objects belonging to 54 different classes including V, n, 8, and other: M, d, Y, R, 6, C, f, s, a, J, y, 2, t, Q, G, K, E, g, k, c, z, 9, p, P, S, and 26 more.

Images in the Captcha Object Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2021.

Explore #

Captcha Object Detection dataset has 100000 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.

Sample annotation mask from Captcha Object Detection

Sample image from Captcha Object Detection

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Have a look at 100000 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 54 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.

Rows 1-10 of 54

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
V➔ rectangle	11593	12229	1.05	12.02%
n➔ rectangle	11567	12177	1.05	10.35%
8➔ rectangle	11547	12148	1.05	10.31%
M➔ rectangle	11541	12226	1.06	14.62%
d➔ rectangle	11530	12193	1.06	10.38%
Y➔ rectangle	11521	12153	1.05	12%
R➔ rectangle	11515	12164	1.06	12.82%
6➔ rectangle	11508	12123	1.05	10.35%
C➔ rectangle	11503	12135	1.05	12.93%
f➔ rectangle	11500	12092	1.05	6.52%

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.

Rows 1-10 of 54

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
V rectangle	12229	11.41%	21.53%	5.12%	41px	68.33%	53px	88.33%	47px	78.84%	12px	7.5%	39px	24.38%
M rectangle	12226	13.81%	27.05%	6.41%	41px	68.33%	53px	88.33%	47px	78.85%	15px	9.38%	49px	30.62%
d rectangle	12193	9.84%	18.22%	4.7%	41px	68.33%	53px	88.33%	47px	78.89%	11px	6.88%	33px	20.62%
n rectangle	12177	9.84%	18.22%	4.27%	41px	68.33%	53px	88.33%	47px	78.82%	10px	6.25%	33px	20.62%
R rectangle	12164	12.16%	23.19%	5.55%	41px	68.33%	53px	88.33%	47px	78.9%	13px	8.12%	42px	26.25%
Y rectangle	12153	11.39%	21.53%	5.12%	41px	68.33%	53px	88.33%	47px	78.92%	12px	7.5%	39px	24.38%
8 rectangle	12148	9.82%	18.22%	4.7%	41px	68.33%	53px	88.33%	47px	78.88%	11px	6.88%	33px	20.62%
C rectangle	12135	12.27%	23.62%	5.55%	41px	68.33%	54px	90%	48px	79.42%	13px	8.12%	42px	26.25%
a rectangle	12134	9.86%	18.22%	4.27%	41px	68.33%	53px	88.33%	47px	79.01%	10px	6.25%	33px	20.62%
2 rectangle	12131	9.86%	18.22%	4.27%	41px	68.33%	53px	88.33%	47px	78.93%	10px	6.25%	33px	20.62%

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.

Objects #

Table contains all 100011 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.

Rows 1-10 of 100011

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	q rectangle	58198.png	60 x 160	49px	81.67%	16px	10%	8.17%
2➔	b rectangle	58198.png	60 x 160	53px	88.33%	22px	13.75%	12.15%
3➔	8 rectangle	58198.png	60 x 160	48px	80%	19px	11.88%	9.5%
4➔	3 rectangle	58198.png	60 x 160	48px	80%	20px	12.5%	10%
5➔	2 rectangle	58198.png	60 x 160	53px	88.33%	21px	13.12%	11.59%
6➔	N rectangle	58198.png	60 x 160	48px	80%	25px	15.62%	12.5%
7➔	p rectangle	58198.png	60 x 160	58px	96.67%	19px	11.88%	11.48%
8➔	6 rectangle	26607.png	60 x 160	41px	68.33%	19px	11.88%	8.11%
9➔	N rectangle	26607.png	60 x 160	41px	68.33%	25px	15.62%	10.68%
10➔	v rectangle	26607.png	60 x 160	48px	80%	21px	13.12%	10.5%

License #

Captcha Object Detection is under 3-Clause BSD License license.

Source

Citation #

If you make use of the Captcha Object Detection data, please cite the following reference:

@dataset{Captcha Object Detection,
  author={Youtham Joseph},
  title={Captcha Object Detection},
  year={2021},
  url={https://www.kaggle.com/datasets/youthamj/captchaobjectdetection}
}

Source

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

@misc{ visualization-tools-for-captcha-object-detection-dataset,
  title = { Visualization Tools for Captcha Object Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/captcha-object-detection } },
  url = { https://datasetninja.com/captcha-object-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Captcha Object Detection 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='Captcha Object Detection', 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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