Summary of Segmentation-free Connectionist Temporal Classification Loss Based Ocr Model For Text Captcha Classification, by Vaibhav Khatavkar et al.
Segmentation-free Connectionist Temporal Classification loss based OCR Model for Text Captcha Classification
by Vaibhav Khatavkar, Makarand Velankar, Sneha Petkar
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel segmentation-free OCR (Optical Character Recognition) model is proposed for text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) classification. By leveraging connectionist temporal classification loss, this model achieves 99.80% character-level accuracy and 95% word-level accuracy on a publicly available dataset. Compared to state-of-the-art models, the proposed approach proves effective in processing variable-length complex CAPTCHAs, paving the way for its massive adoption in securing software systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to recognize text-based CAPTCHAs without breaking them down into smaller parts first. This is important because CAPTCHAs are used to keep computers and humans apart online. The new method uses a special kind of learning algorithm that can understand the order of characters in words. It works really well, getting almost all of the characters right (99.80%) and most of the words right (95%). This could be very useful for keeping software systems safe. |
Keywords
* Artificial intelligence * Classification
