Few-shot symbol classification via self-supervised learning and nearest neighbor

The recognition of symbols within document images is one of the most relevant steps involved in the Document Analysis field. While current state-of-the-art methods based on Deep Learning are capable of adequately performing this task, they generally require a vast amount of data that has to be manually labeled. In this paper, we propose a self-supervised learning-based method that addresses this task by training a neural-based feature extractor with a set of unlabeled documents and performs the recogni-tion task considering just a few reference samples. Experiments on different corpora comprising music, text, and symbol documents report that the proposal is capable of adequately tackling the task with high accuracy rates of up to 95% in few-shot settings. Moreover, results show that the presented strategy out-performs the base supervised learning approaches trained with the same amount of data that, in some cases, even fail to converge. This approach, hence, stands as a lightweight alternative to deal with symbol classification with few annotated data.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

This paper proposes a self-supervised learning-based method for symbol recognition in document images. It trains a neural-based feature extractor with unlabeled documents and performs recognition with only a few reference samples. Experimental results demonstrate that this method achieves high accuracy rates of up to 95% in few-shot settings and outperforms supervised learning approaches using the same amount of data.