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Page Layout Analysis with Graph Neural Networks
 Otčenáš, Matej ; Kišš, Martin (oponent) ; Hradiš, Michal (vedoucí práce)
The aim of this work is to experimentally test the power of graph neural networks in the comprehensive analysis of document layout. In terms of document types, the focus is primarily on newspaper articles and historical writings, such as handwritten books or medieval manuscripts. These are characterized by the complexity of their layout, lacking a fixed structure or having highly segmented text. The work deals with the creation of suitable datasets for training and testing an approach for globally ordering the sequence of reading lines on a page and assigning each line to one of the defined classes. The research also involves creating an appropriate representation of a graph that captures relationships between individual components on the page and selecting a suitable graph neural network with the appropriate parameters. Finally, the different approaches are evaluated and compared on multiple metrics suitable for the given problem, and the findings are summarized with a discussion on possible enhancements and limitations.
Úplný záznam
Graph Neural Networks for Document Analysis
 Patrik, Nikolas ; Španěl, Michal (oponent) ; Hradiš, Michal (vedoucí práce)
In this thesis we use for graph neural networks for document analysis. In the beggining we introduce how these graph convolutional networks work and also we introduce concept which is used for their implementation. Next, we explain current solution that solves semantic labeling of text entities in scanned documents, what is also same as the goal of this thesis. In following chapter we present solution which should be used for the mentioned problem as well as another problem which is extraction of specific data using active learning. Gradually, we explain how this solution was implemented and what tools we have used. Before ending, we show our dataset, we have annotated and we meant to use for evaluation and training of our solution. In the end, we present results of this thesis, compare our model with others and also evaluate how our model was able to extract specified data using active learning.
Úplný záznam

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