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      Evolution maps and applications

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          Abstract

          Common tasks in document analysis, such as binarization, line extraction etc., are still considered difficult for highly degraded text documents. Having reliable fundamental information regarding the characters of the document, such as the distribution of character dimensions and stroke width, can significantly improve the performance of these tasks. We introduce a novel perspective of the image data which maps the evolution of connected components along the change in gray scale threshold. The maps reveal significant information about the sets of elements in the document, such as characters, noise, stains, and words. The information is further employed to improve state of the art binarization algorithm, and achieve automatically character size estimation, line extraction, stroke width estimation, and feature distribution analysis, all of which are hard tasks for highly degraded documents.

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          Most cited references 29

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          Reexamining the word length effect in visual word recognition: new evidence from the English Lexicon Project.

          In the present study, we reexamined the effect of word length (number of letters in a word) on lexical decision. Using the English Lexicon Project, which is based on a large data set of over 40,481 words (Balota et al., 2002), we performed simultaneous multiple regression analyses on a selection of 33,006 English words (ranging from 3 to 13 letters in length). Our analyses revealed an unexpected pattern of results taking the form of a U-shaped curve. The effect of number of letters was facilitatory for words of 3-5 letters, null for words of 5-8 letters, and inhibitory for words of 8-13 letters. We also showed that printed frequency, number of syllables, and number of orthographic neighbors all made independent contributions. The length effects were replicated in a new analysis of a subset of 3,833 monomorphemic nouns (ranging from 3 to 10 letters), and also in another analysis based on 12,987 bisyllabic items (ranging from 3 to 9 letters). These effects were independent of printed frequency, number of syllables, and number of orthographic neighbors. Furthermore, we also observed robust linear inhibitory effects of number of syllables. Implications for models of visual word recognition are discussed.
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            Document image binarization based on texture features

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              Page segmentation using texture analysis

               Anil Jain,  Yu Zhong (1996)
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                Author and article information

                Contributors
                Journal
                peerj-cs
                PeerJ Computer Science
                PeerJ Comput. Sci.
                PeerJ Inc. (San Francisco, USA )
                2376-5992
                6 January 2016
                : 2
                Affiliations
                [1 ]Department of Computer Science, Ben-Guion University of the Negev , Beer-Sheva, Israel
                [2 ]Electrical and Computer Engineering Department, Ben-Guion University of the Negev , Beer-Sheva, Israel
                Article
                cs-39
                10.7717/peerj-cs.39
                © 2016 Biller et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                Product
                Self URI (journal-page): https://peerj.com/computer-science/
                Funding
                Funded by: German Research Foundation
                Award ID: FI 1494/3-2
                This project was funded by the German Research Foundation under contract FI 1494/3-2. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Computer Vision
                Digital Libraries

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