Name
Abstract | ||
|---|---|---|
In previous work, we proposed the application of the Expectation-Maximization (EM) algorithm in the binarization of historical documents by defining a multi-resolution framework. In this work, we extend the multiresolution framework to the Otsu algorithm for effective binarization of historical documents. We compare the effectiveness of the EM based binarization technique to the Otsu thresholding algorithm on historical documents. We demonstrate how the EM can be extended to perform an effective segmentation of historical documents by taking into account multiple features beyond the intensity of the document image. Experimental results, analysis and comparisons to known techniques are presented using the document image collection from the DIBCO 2009 contest. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1117/12.876582 | DOCUMENT RECOGNITION AND RETRIEVAL XVIII |
Keywords | Field | DocType |
image thresholding, historical documents, binarization, document image analysis | Computer vision,Pattern recognition,Document image processing,Thresholding algorithm,Segmentation,Expectation–maximization algorithm,Computer science,Multiresolution analysis,Artificial intelligence,Historical document | Conference |
Volume | ISSN | Citations |
7874 | 0277-786X | 1 |
PageRank | References | Authors |
0.37 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tayo Obafemi-Ajayi | 1 | 25 | 4.83 |
| Gady Agam | 2 | 391 | 43.99 |