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Matching table structures of historical register books using association graphs
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| Florian Kleber, Markus Diem, Hervé Déjean, Jean-Luc Meunier, Eva Lang |
| 16th International Conference on Frontiers in Handwriting Recognition, Niagara Falls, USA, 5-8 August, 2018 |
| Download |
@inproceedings{inproceedings,
author = {Kleber, Florian and Diem, Markus and Dejean, Herve and Meunier, Jean-Luc and Lang, Eva},
year = {2018},
month = {08},
pages = {217-222},
title = {Matching Table Structures of Historical Register Books using Association Graphs},
doi = {10.1109/ICFHR-2018.2018.00046}
}
In this paper we present a template based table structure matching using association graphs for handwritten/printed historical documents. The recognition of the table structure consisting of column and header information is the prerequisite for the subsequent row detection and handwritten text recognition used for information extraction. The table matching is done by detecting the maximimum clique in an association graph which represents the matching of the line information of the template and a document of interest. This allows for variations of widths and heights from rows and columns. The presented methodology is evaluated on historical register books (death records) of the Archive of the Diocese of Passau. The method shows a reliable detection of the structure of handwritten/printed tables with a mean cell match of 88.28%.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
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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