Name
Abstract | ||
|---|---|---|
Layout analysis is a crucial process for document image understanding and information retrieval. Document layout analysis depends on page segmentation and block classification. This paper describes an algorithm for extracting blocks from document images and a boosting based method to classify those blocks as machine printed text or not. The feature vector which is fed into the boosting classifier consists of a four direction run-length histogram, and connected components features in both background and foreground. Using a combination of features through a boosting classifier, we obtain an accuracy of 99.5% on our test collection. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1117/12.876736 | DOCUMENT RECOGNITION AND RETRIEVAL XVIII |
Keywords | Field | DocType |
Document image analysis, layout analysis, zone classification, adaptive boosting | Histogram,Computer vision,Feature vector,Pattern recognition,Computer science,Document clustering,Segmentation,Document layout analysis,Boosting (machine learning),Artificial intelligence,Connected component,Classifier (linguistics) | Conference |
Volume | ISSN | Citations |
7874 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binqing Xie | 1 | 0 | 0.34 |
| Gady Agam | 2 | 391 | 43.99 |