Name
Abstract | ||
|---|---|---|
In this paper we deal with a special case of archive handwritten text recognition when word spotting can be used effectively. We analyze the use of local feature descriptors and show that the Scale Invariant Feature Transform can be used efficiently despite the large variety of word shape, and the effects of different noises. We evaluate the performance on a database of 1638 word records segmented from an archive book and show that the proposed feature processing method can achieve over 80 % hit rate. Different parameter settings and variations of the local feature descriptor are analyzed. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CBMI.2013.6576578 | 2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI) |
Keywords | Field | DocType |
local feature based word spotting,handwritten archive documents,archive handwritten text recognition,local feature descriptors,scale invariant feature transform,word shape,noises,database,word record segmentation,archive book,feature processing method | Hit rate,Scale-invariant feature transform,Pattern recognition,Computer science,Feature (computer vision),Speech recognition,Image segmentation,Artificial intelligence,Spotting,Word processing,Intelligent word recognition,Special case | Conference |
ISSN | ISBN | Citations |
1949-3983 | 978-1-4799-0955-1 | 1 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| László Czuni | 1 | 68 | 13.41 |
| Peter Jozsef Kiss | 2 | 6 | 1.52 |
| Mónika Gál | 3 | 5 | 1.86 |
| Ágnes Lipovits | 4 | 4 | 1.16 |