Name
Abstract | ||
|---|---|---|
A new method is presented for adaptive document image binarization, where the page is considered as a collection of subcomponents such as text, background and picture. The problems caused by noise, illumination and many source type related degradations are addressed. The algo= rithm uses document characteristics to determine (surface) attributes, often used in document segmentation. Using characteristics analysis, two new algorithms are applied to determine a local threshold for each pixel. An algorithm based on soft decision control is used for thresholding background and picture regions. An approach utilizing local mean and variance of gray values is applied to textual regions. Tests were performed with images including different types of document components and degradations. The results show that the method adapts and performs well in each case. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/ICDAR.1997.619831 | ICDAR-1 |
Keywords | Field | DocType |
local threshold,adaptive document image binarization,document component,characteristics analysis,document characteristic,new method,new algorithm,method adapts,local mean,document segmentation,adaptive document binarization,optical character recognition,noise,pixel,image segmentation,machine vision,page,degradation,background,lighting,performance,picture,image analysis,illumination,testing,variance,algorithm design and analysis,histograms,text analysis | Computer vision,Histogram,Scale-space segmentation,Pattern recognition,Computer science,Document segmentation,Document layout analysis,Optical character recognition,Image segmentation,Pixel,Artificial intelligence,Thresholding | Conference |
ISSN | ISBN | Citations |
1520-5363 | 0-8186-7898-4 | 52 |
PageRank | References | Authors |
6.80 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaakko J. Sauvola | 1 | 451 | 44.31 |
| Tapio Seppänen | 2 | 604 | 84.33 |
| Sami Haapakoski | 3 | 52 | 6.80 |
| Matti Pietikäinen | 4 | 14779 | 739.80 |