A Methodology for Deriving Probabilistic Correctness Measures from Recognizers
Abstract
This paper describes the derivation of probability of correctness from scores assigned by most recognizers. Motivation for this research is three-fold: (i) probability values can be used to rerank the output of any recognizer by using a new set of training data; if the training data is sufficiently large and representative of the test data, the recognition rates are seen to improve significantly, (ii) derivation of probability values puts the output of different recognizers on the same scale; this makes comparison across recognizers trivial, and (iii) word recognition can be readily extended to phrase and sentence recognition because the integration of language models becomes straightforward. We have conducted an extensive set of experiments. The results show a reranking of recognition choices based on the derived probability values leading to an enhancement in performance.
Cite
Text
Bouchaffra et al. "A Methodology for Deriving Probabilistic Correctness Measures from Recognizers." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998. doi:10.1109/CVPR.1998.698716Markdown
[Bouchaffra et al. "A Methodology for Deriving Probabilistic Correctness Measures from Recognizers." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998.](https://mlanthology.org/cvpr/1998/bouchaffra1998cvpr-methodology/) doi:10.1109/CVPR.1998.698716BibTeX
@inproceedings{bouchaffra1998cvpr-methodology,
title = {{A Methodology for Deriving Probabilistic Correctness Measures from Recognizers}},
author = {Bouchaffra, Djamel and Govindaraju, Venu and Srihari, Sargur N.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1998},
pages = {930-935},
doi = {10.1109/CVPR.1998.698716},
url = {https://mlanthology.org/cvpr/1998/bouchaffra1998cvpr-methodology/}
}