Name
Abstract | ||
|---|---|---|
This work presents an analytical study on the relevance of features in an existing framework for writer identification from offline handwritten document images. The identification system comprises a set of 15 features combining the orientation and curvature information in a writing with the well-known codebook based approach. This study aims to find the optimal feature subset to identify the author of a questioned document while maintaining acceptable identification rates. Employing a genetic algorithm with a wrapper method we carry out a feature selection mechanism and identify the most relevant features that characterize the writer of a handwritten document. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1117/12.873309 | DOCUMENT RECOGNITION AND RETRIEVAL XVIII |
Keywords | Field | DocType |
Writer Identification, Feature Relevance, Feature Selection, Genetic Algorithms | Feature selection,Computer science,Document image processing,Identification system,Artificial intelligence,Natural language processing,Genetic algorithm,Off line,Pattern recognition,Speech recognition,Feature extraction,Feature relevance,Codebook | Conference |
Volume | ISSN | Citations |
7874 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Imran Siddiqi | 1 | 421 | 36.56 |
| Khurram Khurshid | 2 | 129 | 15.94 |
| Nicole Vincent | 3 | 195 | 12.09 |