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.
Highlights
Segmentation is an important step in image analysis since it detects homogeneous regions whose characteristics can be computed and analyzed, for example, for discriminating between different classes of objects such as faces and nonfaces
The unbiased evaluation of segmentation results is difficult because it requires an ideal description of what one should obtain as the result of segmentation of a certain image regardless of the segmentation algorithm
As one can see from the brief discussion of various ground truthing strategies, one of the first tasks is to choose a proper representation for page regions
Summary
Segmentation is an important step in image analysis since it detects homogeneous regions whose characteristics can be computed and analyzed, for example, for discriminating between different classes of objects such as faces and nonfaces. The unbiased evaluation of segmentation results is difficult because it requires an ideal description of what one should obtain as the result of segmentation of a certain image regardless of the segmentation algorithm This ideal description, known as the ground truth, can be utilized for judging whether segmentation is correct or not, and how well a given image is segmented. The second alternative is automated and more attractive when it is necessary to process a large number of images It uses a special (usually text) file, called a ground truth (GT), for each image, containing a description of different regions that should be detected during correct segmentation. Modern approaches to ground truth generation are briefly reviewed
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