Captcha Recognition Image Dataset
#Image classification #object detection #machine learning model training #deep learning model optimization #Captcha recognition #image recognition systems #automated login systems #security verification optimization
- 500 records
- 1.3G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
This dataset has several core advantages. Firstly, the high precision and consistency of annotations make it advantageous for achieving high accuracy in model training, capable of boosting recognition rates to over 95% compared to the common 90% in the market. Secondly, by introducing new data augmentation techniques, such as rotation and distortion transformations, the model's generalization capabilities are enhanced. Moreover, the captcha image dataset directly addresses the practical and common technical issue of captcha recognition, resulting in significant improvements in user experience and system security. Compared with similar datasets, this dataset has higher weights in terms of sample diversity and difficulty, particularly providing scarce data sources for the more challenging distorted samples. Finally, its organization in a standardized format allows for easy expansion and application to different captcha recognition tasks, showing high versatility.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| captcha_text | string | The captcha text displayed in the image. |
| captcha_length | int | The number of characters in the captcha. |
| captcha_complexity | string | The complexity of the captcha, such as whether it includes uppercase, numbers, special characters, etc. |
| background_noise_level | string | The level of background noise, such as the presence of interfering lines, dots, etc. |
| distortion_type | string | The type of distortion in the captcha image, such as rotation, bending, etc. |
| color_scheme | string | The color scheme of the captcha, such as monochrome or multicolor. |
| text_font_size | int | The font size of the captcha text. |
| text_font_type | string | The font type used for the captcha text. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the purpose of the CAPTCHA Recognition Image Dataset?
- The CAPTCHA Recognition Image Dataset is mainly used to improve the efficiency of CAPTCHA cracking and automated recognition and can be applied to machine learning training.
- How can this dataset be used to enhance the performance of machine learning models?
- By training machine learning models to recognize and crack CAPTCHAs, increasing the diversity of the dataset can improve the model's generalization ability.
- What does the CAPTCHA Recognition Image Dataset include?
- The dataset includes a diverse collection of images for training CAPTCHA recognition and cracking.
- In what situations would the CAPTCHA Recognition Image Dataset be particularly useful?
- The dataset is particularly useful for automating the processing and recognition of complex CAPTCHAs, as well as for researching and developing more intelligent CAPTCHA recognition systems.
- What are the industry applications of the CAPTCHA Recognition Image Dataset?
- The dataset is widely used in fields such as security verification, automated testing, and user experience optimization.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Captcha Recognition Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/18c74f6a1286fd36856c0f6a2a3d8548?dataset_scene_cate_type=9},
urldate={2026-02-04},
keywords={Captcha recognition, captcha cracking, image recognition, automated verification, machine learning captcha},
version={1.0}
}Using this in research? Please cite us.
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