Automated Evaluation of OCR Zoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Page segmentation using the description of the background
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
An Automatic Closed-Loop Methodology for Generating Character Groundtruth for Scanned Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Methodology for Flexible and Efficient Analysis of the Performance of Page Segmentation Algorithms
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Evaluating spatial correspondence of zones in document recognition systems
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Automatic Ground-Truth Generation for Skew-Tolerance Evaluation of Document Layout Analysis Methods
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
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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.