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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ntirogiannis, K. Gatos, B. Pratikakis, I. |
| Copyright Year | 2009 |
| Abstract | In this paper, a new document image binarization technique is presented, as an improved version of the state-of-the-art adaptive logical level technique (ALLT). The original ALLT depends on fixed windows to extract essential features such as the character stroke width. Since characters with several different stroke widths may exist within a region, this can lead to erroneous results. In our approach, we use local adaptive binarization as a guide to our adaptive stroke width detection. The skeleton and the contour points of the binarization output are combined to identify locally the stroke width. Additionally, we introduce an adaptive local parameter “β” that enhances the characters and improves the overall performance. In this way, we achieve more accurate binarization results in both handwritten and printed documents with a particular focus on degraded historical documents. Experimental results prove the effectiveness of the proposed technique compared to other state-of-the-art methodologies. |
| Starting Page | 1171 |
| Ending Page | 1175 |
| File Size | 568699 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424445004 |
| ISSN | 15205363 |
| DOI | 10.1109/ICDAR.2009.225 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-26 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Optical character recognition software Skeleton Text analysis Image analysis Image recognition Computational intelligence Laboratories Informatics Feature extraction Degradation Document preprocessing Adaptive Binarization |
| Content Type | Text |
| Resource Type | Article |
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