Efficient few-shot learning for pixel-precise handwritten document layout analysis

TL;DR

This paper introduces an efficient few-shot learning framework for pixel-precise handwritten document layout analysis, reducing the need for extensive pixel-level annotations while maintaining high performance.

Contribution

It presents a novel few-shot learning approach that achieves state-of-the-art results with minimal labeled data in handwritten document layout analysis.

Findings

Comparable performance to fully supervised methods on DIVA-HisDB

Significantly reduces annotation effort

Effective in real-world scenarios with limited labels

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.