Name
Abstract | ||
|---|---|---|
In this paper, we propose a keyword retrieval system for locating words in historical Mongolian document images. Based on the word spotting technology, a collection of historical Mongolian document images is converted into a collection of word images by word segmentation, and a number of profile-based features are extracted to represent word images. For each word image, a fixed-length feature vector is formulated by obtaining the appropriate number of the complex coefficients of discrete Fourier transform on each profile feature. The system supports online image-to-image matching by calculating similarities between a query word image and each word image in the collection, and consequently, a ranked result is returned in descending order of the similarities. Therein, the query word image can be generated by synthesizing a sequence of glyphs when being retrieved. By experimental evaluations, the performance of the system is confirmed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10032-013-0203-6 | IJDAR |
Keywords | Field | DocType |
discrete fourier transform,query image synthesis,kanjur,profile features,word spotting | Glyph,Feature vector,Pattern recognition,Ranking,Computer science,Text segmentation,Artificial intelligence,Discrete Fourier transform,Spotting,Visual Word | Journal |
Volume | Issue | ISSN |
17 | 1 | 1433-2825 |
Citations | PageRank | References |
11 | 0.69 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongxi Wei | 1 | 35 | 5.71 |
| Guanglai Gao | 2 | 78 | 24.57 |