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arXiv · 2304.13530

Key-value information extraction from full handwritten pages

Solène Tarride·Mélodie Boillet·Christopher Kermorvant
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Abstract

We propose a Transformer-based approach for information extraction from digitized handwritten documents. Our approach combines, in a single model, the different steps that were so far performed by separate models: feature extraction, handwriting recognition and named entity recognition. We compare this integrated approach with traditional two-stage methods that perform handwriting recognition before named entity recognition, and present results at different levels: line, paragraph, and page. Our experiments show that attention-based models are especially interesting when applied on full pages, as they do not require any prior segmentation step. Finally, we show that they are able to learn from key-value annotations: a list of important words with their corresponding named entities. We compare our models to state-of-the-art methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform previous performances on all three datasets.

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Solène Tarride, Mélodie Boillet, Christopher Kermorvant. 2023-04-26. Key-value information extraction from full handwritten pages. https://arxiv.org/abs/2304.13530

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