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- W2007497629 abstract "Detection of characters regions is a meaningful research work for both highlighting region of interest and recognition forfurther information processing. A lot of researches have been performed on character localization and extraction and thisleads to the great needs of performance evaluation scheme to inspect detection algorithms. In this paper, two probabilitymodels are established to accomplish evaluation tasks for different applications respectively. For highlighting region ofinterest, a Gaussian probability model, which simulates the property of a low-pass Gaussian filter of human visionsystem (HVS), was constructed to allocate different weights to different character parts. It reveals the greatest potentialto describe the performance of detectors, especially, when the result detected is an incomplete character, where othermethods cannot effectively work. For the recognition destination, we also introduced a weighted probability model togive an appropriate description for the contribution of detection results to final recognition results. The validity ofperformance evaluation models proposed in this paper are proved by experiments on web images and natural sceneimages. These models proposed in this paper may also be able to be applied in evaluating algorithms of locating otherobjects, like face detection and more wide experiments need to be done to examine the assumption." @default.
- W2007497629 created "2016-06-24" @default.
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- W2007497629 date "2010-02-04" @default.
- W2007497629 modified "2023-09-23" @default.
- W2007497629 title "New performance evaluation models for character detection in images" @default.
- W2007497629 doi "https://doi.org/10.1117/12.840119" @default.
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