Name
Abstract | ||
|---|---|---|
Writer recognition is to identify a person on the basis of handwriting, and great progress has been achieved in the past decades. In this paper, we concentrate ourselves on the issue of off-line text-independent writer recognition by summarizing the state of the art methods from the perspectives of feature extraction and classification. We also exhibit some public datasets and compare the performance of the existing prominent methods. The comparison demonstrates that the performance of the methods based on frequency domain features decreases seriously when the number of writers becomes larger, and that spatial distribution features are superior to both frequency domain features and shape features in capturing the individual traits. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1142/S0218001417560080 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Off-line, text-independent, writer recognition, feature extraction, classification, datasets, performance evaluation | Frequency domain,Off line,Pattern recognition,Handwriting,Feature extraction,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
31 | 5 | 0218-0014 |
Citations | PageRank | References |
4 | 0.39 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Jie Xiong | 1 | 14 | 1.21 |
| Yue Lu | 2 | 1617 | 101.51 |
| patrick s p wang | 3 | 303 | 47.66 |