Name
Abstract | ||
|---|---|---|
This paper proposes an enhancement of our previously presented word segmentation method (ILSPLWseg) [1] by exploiting local spatial features. ILSP-LWseg is based on a gap metric that exploits the objective function of a soft-margin linear SVM that separates successive connected components (CCs). Then a global threshold for the gap metrics is estimated and used to classify the candidate gaps in "within" or "between" words classes. In the proposed enhancement the initial categorization is examined against the local features (i.e. margin and slope of the linear classifier for every pair of CCs in each text line) and a refined classification is applied for each text line. The method was tested on the benchmarking datasets of ICDAR07, ICDAR09 and ICFHR10 handwriting segmentation contests and performs better than the winning algorithm. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDAR.2011.264 | Document Analysis and Recognition |
Keywords | DocType | ISSN |
document image processing,handwritten character recognition,image segmentation,pattern classification,support vector machines,ICDAR07,ICDAR09,ICFHR10,ILSP-LWseg,connected components,gap metric,handwritten word segmentation,linear classifier,local spatial features,refined classification,soft margin linear SVM,document image processing,handwritten word segmentation,support vector machines | Conference | 1520-5363 E-ISBN : 978-0-7695-4520-2 |
ISBN | Citations | PageRank |
978-0-7695-4520-2 | 1 | 0.37 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fotini Simistira | 1 | 1 | 0.37 |
| Vassilis Papavassiliou | 2 | 1 | 0.37 |
| Themos Stafylakis | 3 | 431 | 30.12 |
| Vassilis Katsouros | 4 | 6 | 1.29 |