Name
Abstract | ||
|---|---|---|
Many document images are rich in color and have complex background. To detect text from them, a standard approach utilizes both color and binary information. This often leads to time-consuming processing and requires a lot of parameters to be tuned. In contrast, we propose a new method for text detection using a binary image alone. The main virtues of our method include detection of both normal and inverted text and robustness to various font types, styles and sizes and small skew angles, combined with a moderate number of free parameters. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICPR.2002.1047795 | ICPR (3) |
Keywords | Field | DocType |
free parameter,skew angles,binary information,connected component analysis,normal text,document image,character recognition,color images,text detection,complex background,moderate number,inverted text,main virtue,robust text detection,binarized document images,document image processing,new method,image colour analysis,binary image,robustness,information retrieval,text analysis,image analysis,gray scale,machine vision,parameter estimation | Computer vision,Feature detection (computer vision),Pattern recognition,Computer science,Binary image,Document layout analysis,Image processing,Robustness (computer science),Artificial intelligence,Skew,Digital image processing,Color image | Conference |
Volume | ISSN | ISBN |
3 | 1051-4651 | 0-7695-1695-X |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oleg Okun | 1 | 308 | 28.56 |
| Yu Yan | 2 | 0 | 0.34 |
| Matti Pietikäinen | 3 | 14779 | 739.80 |