We present in this paper experiments on Table Recognition in Handwritten register books. We first explain how the problem of row and column detection is modelled, 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. Software and dataset are open source/date.
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Comparing machine learning approaches for table recognition in historical register books
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| Hervé Déjean, Stéphane Clinchant, Jean-Luc Meunier, Eva Maria Lang, Florian Kleber |
| 13th IAPR International Workshop on Document Analysis Systems, Vienna, Austria, 24-27 April, 2018 |
@inproceedings{clinchant2018comparing, title={Comparing machine learning approaches for table recognition in historical register books}, author={Clinchant, St{\’e}phane and D{\’e}jean, Herv{\’e} and Meunier, Jean-Luc and Lang, Eva Maria and Kleber, Florian}, booktitle={2018 13th IAPR International Workshop on Document Analysis Systems (DAS)}, pages={133–138}, year={2018}, organization={IEEE} }
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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