Newspaper layout analysis incorporating connected component separation

Mitchell, Peter S. R.; Yan, Hong

doi:10.1016/j.imavis.2003.11.001

Cited by 12 publications

(2 citation statements)

References 19 publications

(20 reference statements)

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“…The problem of layout analysis on newspaper data has been addressed by few researchers [8,7,9,10,17,18,20,16]. Gatos et al [8] proposed a two stage technique for layout analysis of newspaper page.…”

Section: Related Workmentioning

confidence: 99%

Text graphic separation in Indian newspapers

Garg

Bansal

Chaudhury

et al. 2013

Proceedings of the 4th International Workshop on Multilingual OCR

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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.

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Section: Related Workmentioning

confidence: 99%

Text graphic separation in Indian newspapers

Garg

Bansal

Chaudhury

et al. 2013

Proceedings of the 4th International Workshop on Multilingual OCR

View full text Add to dashboard Cite

show abstract

“…The first one treats a word as a collection of simpler subunits such as characters and proceeds by segmenting the word into these units, identifying the units and building a word-level interpretation using the lexicon. The second one treats the word as a single, indivisible entity and attempts to recognize it using features of the word as whole [1][2][3][4]. This approach is referred to as the word-based or holistic and is inspired in part by psychological studies of human reading, which indicate that humans use features of word shape such as length, ascenders, and descanters in reading (Fig.…”

Section: Introductionmentioning

confidence: 99%