Document skew estimation without angle range restriction
| Autor: | Matti Pietikäinen, Oleg Okun, Jaakko Sauvola |
|---|---|
| Rok vydání: | 1999 |
| Předmět: |
Covariance matrix
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Pattern recognition Fuzzy logic Computer Science Applications Hough transform law.invention law Robustness (computer science) Computer Vision and Pattern Recognition Artificial intelligence business Image resolution Connected-component labeling Software Eigenvalues and eigenvectors |
| Zdroj: | International Journal on Document Analysis and Recognition. 2:132-144 |
| ISSN: | 1433-2825 1433-2833 |
| DOI: | 10.1007/s100320050043 |
| Popis: | The existing skew estimation techniques usually assume that the input image is of high resolution and that the detectable angle range is limited. We present a more generic solution for this task that overcomes these restrictions. Our method is based on determination of the first eigenvector of the data covariance matrix. The solution comprises image resolution reduction, connected component analysis, component classification using a fuzzy approach, and skew estimation. Experiments on a large set of various document images and performance comparison with two Hough transform-based methods show a good accuracy and robustness for our method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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