Efficient few-shot learning for pixel-precise handwritten document layout analysis
This addresses the labeling bottleneck for ancient handwritten document analysis, making it more practical for real-world scenarios, though it is incremental as it builds on existing few-shot learning ideas.
The paper tackles the problem of pixel-precise layout analysis for handwritten documents, which is time-consuming to label, by proposing an efficient few-shot learning framework that achieves performances comparable to state-of-the-art fully supervised methods on the DIVA-HisDB dataset.
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.