tailieunhanh - Báo cáo hóa học: " Fast and Accurate Ground Truth Generation for Skew-Tolerance Evaluation of Page Segmentation Algorithms"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Fast and Accurate Ground Truth Generation for Skew-Tolerance Evaluation of Page Segmentation Algorithms | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article iD 12093 Pages 1-10 DOI ASP 2006 12093 Fast and Accurate Ground Truth Generation for Skew-Tolerance Evaluation of Page Segmentation Algorithms Oleg Okun and Matti Pietikainen Infotech Oulu and Department of Electrical and Information Engineering Machine Vision Group University of Oulu 4500 FI-90014 Finland Received 15 February 2005 Revised 30 May 2005 Accepted 12 July 2005 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 .