A precise skew estimation algorithm for document images using KNN clustering and Fourier transform
From LRDE
- Authors
- Jonathan Fabrizio
- Where
- Proceedings of the 21st International Conference on Image Processing (ICIP)
- Place
- Paris, France
- Type
- inproceedings
- Projects
- Olena
- Keywords
- Image
- Date
- 2014-05-26
Abstract
In this article, we propose a simple and precise skew estimation algorithm for binarized document images. The estimation is performed in the frequency domain. To get a precise result, the Fourier transform is not applied to the document itself but the document is preprocessed: all regions of the document are clustered using a KNN and contours of grouped regions are smoothed using the convex hull to form more regular shapes, with better orientation. No assumption has been made concerning the nature or the content of the document. This method has been shown to be very accurate and was ranked first at the DISEC'13 contestduring the ICDAR competitions.
Documents
Bibtex (lrde.bib)
@InProceedings{ fabrizio.14.icip, author = {Jonathan Fabrizio}, title = {A precise skew estimation algorithm for document images using {KNN} clustering and Fourier transform}, booktitle = {Proceedings of the 21st International Conference on Image Processing (ICIP)}, year = 2014, address = {Paris, France}, pages = {2585--2588}, abstract = {In this article, we propose a simple and precise skew estimation algorithm for binarized document images. The estimation is performed in the frequency domain. To get a precise result, the Fourier transform is not applied to the document itself but the document is preprocessed: all regions of the document are clustered using a KNN and contours of grouped regions are smoothed using the convex hull to form more regular shapes, with better orientation. No assumption has been made concerning the nature or the content of the document. This method has been shown to be very accurate and was ranked first at the DISEC'13 contest, during the ICDAR competitions.}, doi = {10.1109/ICIP.2014.7025523} }