Name
Abstract | ||
|---|---|---|
Text-independent handwriting identification methods require that features such as texture are extracted from lengthy document image; while text-dependent handwriting identification methods require that the contents of the documents being compared are identical. In order to overcome these confinements, this paper presents a novel Chinese handwriting identification technique. First, Chinese characters are segmented from handwriting document, then keywords are extracted based on matching and voting of local features of character. Then the same-content keywords are used to build training sets, and these training sets of two documents are compared. Because the keywords are similar to signature, the handwriting identification problem is transformed into signature verification problem. Experiments on HIT-MW, HIT-SW and CASIA show this method outperforms many text-independent handwriting identification methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1142/S0218001417530044 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Local feature, reference table, matching and voting, handwritten identification, signature verification | Chinese characters,Voting,Pattern recognition,Intelligent character recognition,Handwriting,Keyword extraction,Computer science,Speech recognition,Artificial intelligence,Natural language processing,Reference table,Parameter identification problem | Journal |
Volume | Issue | ISSN |
31 | 11 | 0218-0014 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Chen | 1 | 22 | 6.77 |
| Bin Fang | 2 | 784 | 53.47 |
| patrick s p wang | 3 | 303 | 47.66 |