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 INCMOBIUSI INC
Updated:2026-03-06

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

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

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Cite this Work

@dataset{Mobiusiundefined,
  title={},
  author={Mobiusi},
  year={undefined},
  url={https://www.mobiusi.com/datasets/18c74f6a1286fd36856c0f6a2a3d8548?dataset_scene_cate_type=9},
  urldate={},
  keywords={},
  version={}
}

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