Name
Abstract | ||
|---|---|---|
In this paper we present an off-line signature verification and recognition system using the global, directional and grid fea-tures of signatures. Support Vector Machine (SVM) was used to verify and classify the signatures and a classification ratio of 0.95 was obtained. As the recognition of signatures repre-sents a multiclass problem SVM's one-against-all method was used. We also compare our methods performance with Artifical Neural Network's (ANN) backpropagation method. |
Year | Venue | Keywords |
|---|---|---|
2005 | Antalya | backpropagation,feature extraction,handwriting recognition,handwritten character recognition,neural nets,support vector machines,ANN backpropagation method,SVM,artifical neural network backpropagation method,off-line signature verification,signature recognition,signatures grid features,support vector machine |
Field | DocType | ISBN |
Structured support vector machine,Histogram,Data mining,Off line,Pattern recognition,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Backpropagation,Artificial neural network,Grid | Conference | 978-160-4238-21-1 |
Citations | PageRank | References |
19 | 0.98 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emre Özgündüz | 1 | 19 | 0.98 |
| Tülin Sentürk | 2 | 19 | 0.98 |
| M. Elif Karsligil | 3 | 73 | 13.69 |