Review Article
A Survey on Breaking Technique of Text-Based CAPTCHA
Table 3
Comparison of typical methods based on segmentation for breaking adherent CAPTCHA.
| Example | Source | Success rate | Reference | Breaking method | Year |
| | Google, Yahoo | 4.89%–66.2% | [2] | Segmentation: width Recognition: CNN | 2004 |
| | Microsoft Google Yahoo | 61% 8.7% 25.9% | [4] | Segmentation: color filling and projection Recognition: CNN | 2008 |
| | Hotmail | 40% | [5] | Segmentation: change width Recognition: SVM Post-processing: DP search | 2009 |
| | MSN Yahoo | 18% 45% | [6] | Segmentation: projection and central | 2010 |
| | Megaupload | 78% | [36] | Segmentation: color filling Combination: nonredundancy Recognition: CNN | 2010 |
| | reCAPT-CHA Google | 33% 46.75% | [38] | Segmentation: character structure feature Recognition: CNN | 2011 |
| | Yahoo | 54.7% | [44] | Segmentation: projection and character feature Recognition: OCR | 2012 |
| | Yahoo | 36%–89% | [41] | Segmentation: color filling Combination: redundancy Recognition: CNN Postprocessing: DFS | 2013 |
| | Microsoft | 5.56% | [60] | Different width/location segmenting and template matching | 2015 | 57.05% |
| | reCAPT-CHA | 40.4%–94.3% | [61] | Segmentation: trichromatic code | 2015 | Recognition: SVM |
| | Yahoo | 57.3%–76.7% | [7] | Edge and fuzzy logic segmentation and recognition | 2015 |
| | Microsoft | 5%–77.2% | [42] | Segmentation: Log-Gabor filter Combination: redundancy Recognition: KNN Postprocessing: DP search | 2016 |
| | MSN | 27.1%–53.2% | [48] | Segmentation: different width Recognition: BPNN | 2016 |
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Note. CNN: convolutional neural network, DP: dynamic programming, OCR: optical character recognition, DFS: depth first search, KNN: -nearest neighbor, BPNN: back-propagation neural network.
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