Content directed enhancement of degraded document images

Newspaper Article Extraction Using Hierarchical Fixed Point Model

Abstract: This paper presents a novel learning based framework to extract articles from newspaper images using a Fixed-Point Model. The input to the system comprises blocks of text and graphics, obtained using standard image processing techniques. The fixed point model 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. We use a hierarchical model which works in two stages. In the first stage, a semantic label (heading, sub-heading, text-blocks, image and caption) is assigned to each segmented block. The labels are then used as input to the next stage to group the related blocks into news articles. Experimental results show the applicability of our algorithm in newspaper labeling and article extraction.

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

Abstract: Degraded character recognition is one of the most challenging topic in the field of Kannada character recognition. The degraded characters which are broken and deformed will have missing features and will be difficult for any recognition method. Rebuilding the degraded character is very important for better recognition. This paper proposes a novel method to rebuild the broken characters. These characters are thinned and the endpoints of the lines are obtained. The line segments are effectively rebuilt so as to preserve the degraded character. Experimental results on this method are presented to establish its efficiency.

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

Abstract: Performance of most of the recognition engines for document images is effected by quality of the image being processed and the selection of parameter values for the pre-processing algorithm. Usually the choice of such parameters is done empirically. In this paper, we propose a novel framework for automatic selection of optimal parameters for pre-processing algorithm by estimating the quality of the document image. Recognition accuracy can be used as a metric for document quality assessment. We learn filters that capture the script properties and degradation to predict recognition accuracy. An EM based framework has been formulated to iteratively learn optimal parameters for document image pre-processing. In the E-step, we estimate the expected accuracy using the current set of parameters and filters. In the M-step we compute parameters to maximize the expected recognition accuracy found in E-step. The experiments validate the efficacy of the proposed methodology for document image pre-processing applications.

Text graphic separation in Indian newspapers

Abstract: Digitization of newspaper article is important for registering historical events. Layout analysis of Indian newspaper is a challenging task due to the presence of different font size, font styles and random placement of text and non-text regions. In this paper we propose a novel framework for learning optimal parameters for text graphic separation in the presence of complex layouts. The learning problem has been formulated as an optimization problem using EM algorithm to learn optimal parameters depending on the nature of the document content.

Adaptive document image binarization

Abstract: A new method is presented for adaptive document image binarization, where the page is considered as a collection of subcomponents such as text, background and picture. The problems caused by noise, illumination and many source type-related degradations are addressed. Two new algorithms are applied to determine a local threshold for each pixel. The performance evaluation of the algorithm utilizes test images with ground-truth, evaluation metrics for binarization of textual and synthetic images, and a weight-based ranking procedure for the "nal result presentation. The proposed algorithms were tested with images including di!erent types of document components and degradations. The results were compared with a number of known techniques in the literature. The benchmarking results show that the method adapts and performs well in each case qualitatively and quantitatively. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

A robust algorithm for text string separation from mixed text/graphics images

Abstract: The development and implementation of an algorithm for automated text string separation that is relatively independent of changes in text font style and size and of string orientation are described. It is intended for use in an automated system for document analysis. The principal parts of the algorithm are the generation of connected components and the application of the Hough transform in order to group components into logical character strings that can then be separated from the graphics. The algorithm outputs two images, one containing text strings and the other graphics. These images can then be processed by suitable character recognition and graphics recognition systems. The performance of the algorithm, both in terms of its effectiveness and computational efficiency, was evaluated using several test images and showed superior performance compared to other techniques. >

Adaptive degraded document image binarization

Abstract: This paper presents a new adaptive approach for the binarization and enhancement of degraded documents. The proposed method does not require any parameter tuning by the user and can deal with degradations which occur due to shadows, non-uniform illumination, low contrast, large signal-dependent noise, smear and strain. We follow several distinct steps: a pre-processing procedure using a low-pass Wiener filter, a rough estimation of foreground regions, a background surface calculation by interpolating neighboring background intensities, a thresholding by combining the calculated background surface with the original image while incorporating image up-sampling and finally a post-processing step in order to improve the quality of text regions and preserve stroke connectivity. After extensive experiments, our method demonstrated superior performance against four (4) well-known techniques on numerous degraded document images.

Block segmentation and text extraction in mixed text/image documents

Abstract: The segmentation and classification of digitized printed documents into regions of text and images is a necessary first processing step in document analysis systems. It is shown that a constrained run length algorithm is well suited to partition most documents into areas of text lines, solid black lines, and rectangular ☐es enclosing graphics and halftone images. During the processing these areas are labeled and meaningful features are calculated. By making use of the regular appearance of text lines as textured stripes, a linear adaptive classification scheme is constructed to discriminate text regions from others.