  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.
