Name
Abstract | ||
|---|---|---|
•A system to predict gender from images of handwriting using textural descriptors.•Multiple classifiers to discriminate male and female writings.•Classifiers combined using bagging, voting and stacking techniques.•Generic and script-independent approach applied to English and Arabic handwritings.•Improved results on the QUWI database once compared to state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.eswa.2017.05.033 | Expert Systems with Applications |
Keywords | Field | DocType |
Handwritten documents,Gender classification,Classifier combination,Textural features | Decision tree,Handwriting,Pattern recognition,Segmentation,Computer science,Support vector machine,Local binary patterns,Histogram of oriented gradients,Artificial intelligence,Artificial neural network,Random forest,Machine learning | Journal |
Volume | Issue | ISSN |
85 | C | 0957-4174 |
Citations | PageRank | References |
8 | 0.51 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahreen Ahmed | 1 | 9 | 0.85 |
| Asma Ghulam Rasool | 2 | 8 | 0.51 |
| Hammad Afzal | 3 | 41 | 11.31 |
| Imran Siddiqi | 4 | 421 | 36.56 |