JOURNAL ARTICLE

<title>Segmentation of scanned document images for efficient compression</title>

Hei Tao FungKevin J. Parker

Year: 1996 Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Vol: 2727 Pages: 701-712   Publisher: SPIE

Abstract

A scanned, complex document image may be composed of text, graphics, halftones, and pictures, whose layout is unknown. In this paper, we propose a novel segmentation scheme for scanned document images that facilitates their efficient compression. Our scheme segments an input image into binarizable components and no-binarizable components. By a binarizable component we mean that the region can be represented by no more than two gray levels (or colors) with acceptable perceptual quality. A non-binarizable component is defined as region that has to be represented by more than two gray levels (or colors) with acceptable perceptual quality. Once the components are identified, the binarizable components can be thresholded and compressed as a binary image using an efficient binary encoding scheme together with the gray values represented by the black and white pixels of the binary image. The non-binarizable components can be compressed using another suitable encoding scheme.

Keywords:
Artificial intelligence Computer science Computer vision Pixel Segmentation Binary image Image segmentation Binary number Graphics Data compression Pattern recognition (psychology) Grayscale Encoding (memory) Image compression Image (mathematics) Image processing Computer graphics (images) Mathematics Arithmetic

Metrics

5
Cited By
0.48
FWCI (Field Weighted Citation Impact)
0
Refs
0.62
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Handwritten Text Recognition Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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