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Proceedings ArticleDOI

Content directed enhancement of degraded document images

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TLDR
This paper presents a novel framework that learns optimal parameters, depending on the nature of the document image content for binarization and text/graphics segmentation, using EM algorithm.
Abstract
Most of the document pre-processing techniques are parameter dependent. In this paper, we present a novel framework that learns optimal parameters, depending on the nature of the document image content for binarization and text/graphics segmentation. The learning problem has been formulated as an optimization problem using EM algorithm to adaptively learn optimal parameters. Experimental results have established the effectiveness of our approach.

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Citations
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Proceedings ArticleDOI

Newspaper Article Extraction Using Hierarchical Fixed Point Model

TL;DR: A novel learning based framework to extract articles from newspaper images using a Fixed-Point Model that uses contextual information and features of each block to learn the layout of newspaper images and attains a contraction mapping to assign a unique label to every block.
Proceedings ArticleDOI

A novel local enhancement technique for rebuilding Broken characters in a degraded Kannada script

TL;DR: A novel method to rebuild the broken characters are thinned and the endpoints of the lines are obtained and the line segments are effectively rebuilt so as to preserve the degraded character.
Proceedings ArticleDOI

Automatic Selection of Parameters for Document Image Enhancement Using Image Quality Assessment

TL;DR: A novel framework for automatic selection of optimal parameters for pre-processing algorithm by estimating the quality of the document image and compute parameters to maximize the expected recognition accuracy found in E-step.
Proceedings ArticleDOI

Text graphic separation in Indian newspapers

TL;DR: A novel framework for learning optimal parameters for text graphic separation in the presence of complex layouts of Indian newspaper is proposed.
References
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Journal ArticleDOI

Unified formulation of a class of image thresholding techniques

TL;DR: It is shown that Otsu's image thresholding, Kittler and Illingworth's minimum errorresholding, and Huang and Wang's fuzzy thresholding methods can be derived under a similar mathematical formulation.
Journal ArticleDOI

Text extraction using pyramid

TL;DR: A system using pyramid to extract text strings from a mixed text/graphics image, such as a road map, is described, able to isolate the text from the graphics so that practical electronic versions of each kind can be treated and processed independently.
Proceedings ArticleDOI

Contextual restoration of severely degraded document images

TL;DR: This work proposes an approach to restore severely degraded document images using a probabilistic context model that works well with document collections such as books, even with severe degradations, and hence is ideally suited for repositories such as digital libraries.
Proceedings ArticleDOI

Segmentation of Text and Graphics from Document Images

TL;DR: This work proposes a robust technique for segmenting all sorts of graphics and texts in any orientation from document pages, essential for better OCR performance and vectorization in computer vision applications.
Journal ArticleDOI

Segmentation and classification of mixed text/graphics/image documents

TL;DR: A feature-based document analysis system is presented which utilizes domain knowledge to segment and classify mixed text/graphics/image documents and proper use of domain knowledge is proved to be effective in accelerating the segmentation speed and decreasing the classification error.
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