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arXiv · 1906.11901

Comparing Machine Learning Approaches for Table Recognition in Historical Register Books

Stéphane Clinchant·Hervé Déjean·Jean-Luc Meunier·Eva Lang·Florian Kleber
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Abstract

We present in this paper experiments on Table Recognition in hand-written registry books. We first explain how the problem of row and column detection is modeled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation was conducted on death records provided by the Archive of the Diocese of Passau. Both methods show similar results, a 89 F1 score, a quality which allows for Information Extraction. Software and dataset are open source/data.

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Stéphane Clinchant, Hervé Déjean, Jean-Luc Meunier, Eva Lang, Florian Kleber. 2019-06-14. Comparing Machine Learning Approaches for Table Recognition in Historical Register Books. https://arxiv.org/abs/1906.11901

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