Paper Info
Title
Adaptive document image binarization
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. Two new algorithms are applied to determine a local threshold for each pixel. The performance evaluation of the algorithm utilizes test images with ground-truth, evaluation metrics for binarization of textual and synthetic images, and a weight-based ranking procedure for the final result presentation. The proposed algorithms were tested with images including different types of document components and degradations. The results were compared with a number of known techniques in the literature. The benchmarking results show that the method adapts and performs well in each case qualitatively and quantitatively.
Year
DOI
Venue
2000
10.1016/S0031-3203(99)00055-2
Pattern Recognition
Keywords
Field
DocType
Adaptive binarization,Soft decision,Document segmentation,Document analysis,Document understanding
Computer vision,Document analysis,Pattern recognition,Ranking,Computer science,Document segmentation,Artificial intelligence,Pixel,Ancient document,Benchmarking
Journal
Volume
Issue
ISSN
33
2
0031-3203
Citations 
PageRank 
References 
335
20.85
17
Authors
2
Search Limit
100335
Name
Order
Citations
PageRank
Jaakko J. Sauvola145144.31
Matti Pietikäinen214779739.80