Open Access

Fast and Accurate Ground Truth Generation for Skew-Tolerance Evaluation of Page Segmentation Algorithms

EURASIP Journal on Advances in Signal Processing20062006:012093

https://doi.org/10.1155/ASP/2006/12093

Received: 15 February 2005

Accepted: 12 July 2005

Published: 12 March 2006

Abstract

Many image segmentation algorithms are known, but often there is an inherent obstacle in the unbiased evaluation of segmentation quality: the absence or lack of a common objective representation for segmentation results. Such a representation, known as the ground truth, is a description of what one should obtain as the result of ideal segmentation, independently of the segmentation algorithm used. The creation of ground truth is a laborious process and therefore any degree of automation is always welcome. Document image analysis is one of the areas where ground truths are employed. In this paper, we describe an automated tool called GROTTO intended to generate ground truths for skewed document images, which can be used for the performance evaluation of page segmentation algorithms. Some of these algorithms are claimed to be insensitive to skew (tilt of text lines). However, this fact is usually supported only by a visual comparison of what one obtains and what one should obtain since ground truths are mostly available for upright images, that is, those without skew. As a result, the evaluation is both subjective; that is, prone to errors, and tedious. Our tool allows users to quickly and easily produce many sufficiently accurate ground truths that can be employed in practice and therefore it facilitates automatic performance evaluation. The main idea is to utilize the ground truths available for upright images and the concept of the representative square [9] in order to produce the ground truths for skewed images. The usefulness of our tool is demonstrated through a number of experiments with real-document images of complex layout.

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Authors’ Affiliations

(1)
Infotech Oulu and Department of Electrical and Information Engineering, Machine Vision Group, University of Oulu

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Copyright

© Okun and Pietikäinen 2006