Name
Abstract | ||
|---|---|---|
In this work, we propose a novel algorithm to extract form structure from form documents. First, an effective image binarization method is proposed as a preprocessor for the purpose of color quantization, which is able to deal with complex images by adaptively taking the global and local image characteristics into account. Then, a form feature extraction unit modeled by Markov random field (MRF) is followed to extract the form feature. It estimates the probability of each pixel from its neighborhood state, and recognizes the form structure based on the probability result. Unlike the geometric-based method in literature, our probability-based form feature extraction algorithm is more effective and can successfully recognize any size of form cells. In the experiment, our method has been integrated into a digital archiving system to evaluate its effectiveness in terms of both the visual and f-measurement numerical performance. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5651971 | ICIP |
Keywords | Field | DocType |
gibbs random field,probability estimation,document images,probability-based form feature extraction algorithm,quantisation (signal),digital archiving,form feature extraction unit,form removal,form detection,image recognition,feature extraction,form extraction,object detection,image binarization,digital archiving system,f-measurement numerical performance,markov random field,markov processes,effective image binarization method,information retrieval systems,document image processing,color quantization,image colour analysis,probability,pattern recognition,algorithm design and analysis,pixel,classification algorithms,random field | Computer vision,Object detection,Markov process,Algorithm design,Pattern recognition,Computer science,Markov random field,Feature extraction,Preprocessor,Artificial intelligence,Pixel,Color quantization | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tien-Ying Kuo | 1 | 148 | 19.24 |
| Yi-Chung Lo | 2 | 32 | 5.06 |