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Parameter free approach for segmenting complex manhattan layouts

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Abstract

This paper presents a two-stage parameter-free technique for the physical layout analysis of a document. In the first stage, Gaussian Mixture Model (GMM) with Expectation-Maximization (EM) is applied followed by recursive merging to obtain the best number of components from the height-frequency data. Such components are classified into running text, titles, and graphical elements. Using a Next Nearest Neighbor analysis, running-text and title text are grouped into blocks in the initial layout. At the second stage, the graphical elements are further divided into text boxes, light-colored text on a dark background, line separators, and graphics that give the final layout. Our proposed method achieved an accuracy of 86.30% and 75.14% in recognizing text and non-text elements from our generated dataset, which contains over 700 documents. Results on the ICDAR dataset show accuracy comparable to some of the best and most popular algorithms such as MHS (winner of the ICDAR-RDCL2015 competition) and PRImA’s Aletheia. The strength of our algorithm is that it is entirely free of manually tuned parameters.

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  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

    Laiphangbam Melinda & Chakravarthy Bhagvati

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  1. Laiphangbam Melinda
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  2. Chakravarthy Bhagvati
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Correspondence to Laiphangbam Melinda.

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Melinda, L., Bhagvati, C. Parameter free approach for segmenting complex manhattan layouts. Multimed Tools Appl 82, 6581–6603 (2023). https://doi.org/10.1007/s11042-022-13400-2

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