Writer Identification and Verification Using GMM Supervectors
Abstract
This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the CVL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors.
Cite
Text
Christlein et al. "Writer Identification and Verification Using GMM Supervectors." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6835995Markdown
[Christlein et al. "Writer Identification and Verification Using GMM Supervectors." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/christlein2014wacv-writer/) doi:10.1109/WACV.2014.6835995BibTeX
@inproceedings{christlein2014wacv-writer,
title = {{Writer Identification and Verification Using GMM Supervectors}},
author = {Christlein, Vincent and Bernecker, David and Hönig, Florian and Angelopoulou, Elli},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {998-1005},
doi = {10.1109/WACV.2014.6835995},
url = {https://mlanthology.org/wacv/2014/christlein2014wacv-writer/}
}