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Proceedings Paper

A Markov chain based line segmentation framework for handwritten character recognition
Author(s): Yue Wu; Shengxin Zha; Huaigu Cao; Daben Liu; Premkumar Natarajan
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Paper Abstract

In this paper, we present a novel text line segmentation framework following the divide-and-conquer paradigm: we iteratively identify and re-process regions of ambiguous line segmentation from an input document image until there is no ambiguity. To detect ambiguous line segmentation, we introduce the use of two complimentary line descriptors, referred as to the underline and highlight line descriptors, and identify ambiguities when their patterns mismatch. As a result, we can easily identify already good line segmentations, and largely simplify the original line segmentation problem by only reprocessing ambiguous regions. We evaluate the performance of the proposed line segmentation framework using the ICDAR 2009 handwritten document dataset, and it is close to top-performing systems submitted to the competition. Moreover, the proposed method is also robust against skewness, noise, variable line heights and touching characters. The proposed idea can also be applied to other text analysis tasks such as word segmentation and page layout analysis.

Paper Details

Date Published: 24 March 2014
PDF: 12 pages
Proc. SPIE 9021, Document Recognition and Retrieval XXI, 90210C (24 March 2014); doi: 10.1117/12.2042600
Show Author Affiliations
Yue Wu, Raytheon BBN Technologies (United States)
Shengxin Zha, Raytheon BBN Technologies (United States)
Northwestern Univ. (United States)
Huaigu Cao, Raytheon BBN Technologies (United States)
Daben Liu, Raytheon BBN Technologies (United States)
Premkumar Natarajan, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 9021:
Document Recognition and Retrieval XXI
Bertrand Coüasnon; Eric K. Ringger, Editor(s)

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