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Proceedings Paper

New performance evaluation models for character detection in images
Author(s): YanWei Wang; XiaoQing Ding; ChangSong Liu; Kongqiao Wang
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Paper Abstract

Detection of characters regions is a meaningful research work for both highlighting region of interest and recognition for further information processing. A lot of researches have been performed on character localization and extraction and this leads to the great needs of performance evaluation scheme to inspect detection algorithms. In this paper, two probability models are established to accomplish evaluation tasks for different applications respectively. For highlighting region of interest, a Gaussian probability model, which simulates the property of a low-pass Gaussian filter of human vision system (HVS), was constructed to allocate different weights to different character parts. It reveals the greatest potential to describe the performance of detectors, especially, when the result detected is an incomplete character, where other methods cannot effectively work. For the recognition destination, we also introduced a weighted probability model to give an appropriate description for the contribution of detection results to final recognition results. The validity of performance evaluation models proposed in this paper are proved by experiments on web images and natural scene images. These models proposed in this paper may also be able to be applied in evaluating algorithms of locating other objects, like face detection and more wide experiments need to be done to examine the assumption.

Paper Details

Date Published: 10 February 2010
PDF: 8 pages
Proc. SPIE 7540, Imaging and Printing in a Web 2.0 World; and Multimedia Content Access: Algorithms and Systems IV, 754006 (10 February 2010); doi: 10.1117/12.840119
Show Author Affiliations
YanWei Wang, Tsinghua Univ. (China)
XiaoQing Ding, Tsinghua Univ. (China)
ChangSong Liu, Tsinghua Univ. (China)
Kongqiao Wang, Nokia Research Ctr. (China)


Published in SPIE Proceedings Vol. 7540:
Imaging and Printing in a Web 2.0 World; and Multimedia Content Access: Algorithms and Systems IV
Theo Gevers; Qian Lin; Raimondo Schettini; Zhigang Fan; Cees Snoek, Editor(s)

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