Efficient Few-Shot Learning for Pixel-Precise Handwritten Document Layout Analysis
Abstract
Layout analysis is a task of uttermost importance in ancient handwritten document analysis and represents a fundamental step toward the simplification of subsequent tasks such as optical character recognition and automatic transcription. However, many of the approaches adopted to solve this problem rely on a fully supervised learning paradigm. While these systems achieve very good performance on this task, the drawback is that pixel-precise text labeling of the entire training set is a very time-consuming process, which makes this type of information rarely available in a real-world scenario. In the present paper, we address this problem by proposing an efficient few-shot learning framework that achieves performances comparable to current state-of-the-art fully supervised methods on the publicly available DIVA-HisDB dataset
Cite
Text
De Nardin et al. "Efficient Few-Shot Learning for Pixel-Precise Handwritten Document Layout Analysis." Winter Conference on Applications of Computer Vision, 2023.Markdown
[De Nardin et al. "Efficient Few-Shot Learning for Pixel-Precise Handwritten Document Layout Analysis." Winter Conference on Applications of Computer Vision, 2023.](https://mlanthology.org/wacv/2023/nardin2023wacv-efficient/)BibTeX
@inproceedings{nardin2023wacv-efficient,
title = {{Efficient Few-Shot Learning for Pixel-Precise Handwritten Document Layout Analysis}},
author = {De Nardin, Axel and Zottin, Silvia and Paier, Matteo and Foresti, Gian Luca and Colombi, Emanuela and Piciarelli, Claudio},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2023},
pages = {3680-3688},
url = {https://mlanthology.org/wacv/2023/nardin2023wacv-efficient/}
}