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Proceedings Paper

Text Line Detection Based on Cost Optimized Local Text Line Direction Estimation
Author(s): Yandong Guo; Yufang Sun; Peter Bauer; Jan P. Allebach; Charles A. Bouman
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Paper Abstract

Text line detection is a critical step for applications in document image processing. In this paper, we propose a novel text line detection method. First, the connected components are extracted from the image as symbols. Then, we estimate the direction of the text line in multiple local regions. This estimation is, for the first time, to our knowledge, formulated in a cost optimization framework. We also propose an efficient way to solve this optimization problem. Afterwards, we consider symbols as nodes in a graph, and connect symbols based on the local text line direction estimation results. Last, we detect the text lines by separating the graph into subgraphs according to the nodes’ connectivities. Preliminary experimental results demonstrate that our proposed method is very robust to non-uniform skew within text lines, variability of font sizes, and complex structures of layout. Our new method works well for documents captured with flat-bed and sheet-fed scanners, mobile phone cameras, and with other general imaging assets.

Paper Details

Date Published: 8 February 2015
PDF: 7 pages
Proc. SPIE 9395, Color Imaging XX: Displaying, Processing, Hardcopy, and Applications, 939507 (8 February 2015); doi: 10.1117/12.2083709
Show Author Affiliations
Yandong Guo, Purdue Univ. (United States)
Yufang Sun, Purdue Univ. (United States)
Peter Bauer, Hewlett-Packard Co. (United States)
Jan P. Allebach, Purdue Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 9395:
Color Imaging XX: Displaying, Processing, Hardcopy, and Applications
Reiner Eschbach; Gabriel G. Marcu; Alessandro Rizzi, Editor(s)

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