Text extraction and document image segmentation using matched wavelets and MRF model.
In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.
Kumar
Sunil
S
IBM India Research Laboratory, Delhi-110 016, India. sunil_narang@rediffmail.com
Gupta
Rajat
R
Khanna
Nitin
N
Chaudhury
Santanu
S
Joshi
Shiv Dutt
SD
D000465:Algorithms; D001185:Artificial Intelligence; D004282:Documentation / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011327:Printing / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity