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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kumar, J. Kasar, T. Ramakrishnan, A.G. |
| Copyright Year | 2007 |
| Description | Author affiliation: Indian Inst. of Sci., Bangalore (Kumar, J.) |
| Abstract | Skew correction of complex document images is a difficult task. We propose an edge-based connected component approach for robust skew correction of documents with complex layout and content. The algorithm essentially consists of two steps - an 'initialization' step to determine the image orientation from the centroids of the connected components and a 'search' step to find the actual skew of the image. During initialization, we choose two different sets of points regularly spaced across the the image, one from the left to right and the other from top to bottom. The image orientation is determined from the slope between the two succesive nearest neighbors of each of the points in the chosen set. The search step finds succesive nearest neighbors that satisfy the parameters obtained in the initialization step. The final skew is determined from the slopes obtained in the 'search' step. Unlike other connected component based methods, the proposed method does not require any binarization step that generally precedes connected component analysis. The method works well for scanned documents with complex layout of any skew with a precision of 0.5 degrees. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 680913 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424412716 |
| DOI | 10.1109/TENCON.2007.4429083 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-10-30 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Nearest neighbor searches Histograms Robustness Neural networks Image edge detection Biomedical imaging Biomedical engineering Laboratories Text processing Clustering methods |
| Content Type | Text |
| Resource Type | Article |
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