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Proceedings Paper

Document image cleanup and binarization
Author(s): Victor Wu; Raghaven Manmatha
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Paper Abstract

Image binarization is a difficult task for documents with text over textured or shaded backgrounds, poor contrast, and/or considerable noise. Current optical character recognition (OCR) and document analysis technology do not handle such documents well. We have developed a simple yet effective algorithm for document image clean-up and binarization. The algorithm consists of two basic steps. In the first step, the input image is smoothed using a low-pass filter. The smoothing operation enhances the text relative to any background texture. This is because background texture normally has higher frequency than text does. The smoothing operation also removes speckle noise. In the second step, the intensity histogram of the smoothed image is computed and a threshold automatically selected as follows. For black text, the first peak of the histogram corresponds to text. Thresholding the image at the value of the valley between the first and second peaks of the histogram binarizes the image well. In order to reliably identify the valley, the histogram is smoothed by a low-pass filter before the threshold is computed. The algorithm has been applied to some 50 images from a wide variety of source: digitized video frames, photos, newspapers, advertisements in magazines or sales flyers, personal checks, etc. There are 21820 characters and 4406 words in these images. 91 percent of the characters and 86 percent of the words are successfully cleaned up and binarized. A commercial OCR was applied to the binarized text when it consisted of fonts which were OCR recognizable. The recognition rate was 84 percent for the characters and 77 percent for the words.

Paper Details

Date Published: 1 April 1998
PDF: 11 pages
Proc. SPIE 3305, Document Recognition V, (1 April 1998); doi: 10.1117/12.304638
Show Author Affiliations
Victor Wu, Univ. of Massachusetts/Amherst (United States)
Raghaven Manmatha, Univ. of Massachusetts/Amherst (United States)


Published in SPIE Proceedings Vol. 3305:
Document Recognition V
Daniel P. Lopresti; Jiangying Zhou, Editor(s)

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