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arXiv · 2012.08191

docExtractor: An off-the-shelf historical document element extraction

Tom Monnier·Mathieu Aubry
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Abstract

We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents without requiring any real data annotation. We demonstrate it provides high-quality performances as an off-the-shelf system across a wide variety of datasets and leads to results on par with state-of-the-art when fine-tuned. We argue that the performance obtained without fine-tuning on a specific dataset is critical for applications, in particular in digital humanities, and that the line-level page segmentation we address is the most relevant for a general purpose element extraction engine. We rely on a fast generator of rich synthetic documents and design a fully convolutional network, which we show to generalize better than a detection-based approach. Furthermore, we introduce a new public dataset dubbed IlluHisDoc dedicated to the fine evaluation of illustration segmentation in historical documents.

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Tom Monnier, Mathieu Aubry. 2020-12-15. docExtractor: An off-the-shelf historical document element extraction. https://doi.org/10.1109/icfhr2020.2020.00027

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