Name
Abstract | ||
|---|---|---|
Large-scale digitisation of historical documents demands robust methods that cope with the presence of frequent distortions and noisy artefacts. This paper presents a hybrid text line segmentation method that uses a novel data structure and a rule base to combine the strengths of top-down and bottom-up approaches while minimising their weaknesses. The effectiveness of the proposed approach has been methodically evaluated in the context of large-scale digitisation using a standardised framework. Results on a diverse dataset show improved performance over top-down and bottom-up approaches as well as over a leading commercially available system. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | hybrid text line segmentation method,bottom-up approach,knowledge based systems,historical document,distortion,image segmentation,data structures,noisy artefacts,data structure,top-down approach,rule base,digitisation,text analysis |
Field | DocType | ISSN |
Data mining,Computer vision,Data structure,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Knowledge-based systems,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Distortion | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 2 | 0.36 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Clausner | 1 | 44 | 8.49 |
| Apostolos Antonacopoulos | 2 | 378 | 36.45 |
| stefan pletschacher | 3 | 216 | 20.78 |