Original title: Enhancing TableNet´s Validation: Evaluating the Accuracy of the Existing Architecture with Novel Training Data
Authors: SAHUKARA, Krishna Sai
Document type: Master’s theses
Year: 2023
Language: eng
Abstract: This thesis introduces a innovative approach to automatically create annotated datasets in which different table layouts are systematically generated along with corresponding ground truth coordinates to enhance table detection in images. This thesis aims to implement the ex- isting deep learning architecture (TableNet), renowned for its table detection capabilities, and assess its performance on the established marmot dataset and our newly generated dataset through Bitwise XOR and Intersection over Union (IOU) metrics. This novel dataset facilitates enhanced evaluation of current state-of-the-art architectures and empowers the development of more effective models through comprehensive training. This evaluation seeks to assess the effectiveness of the proposed dataset generation method in improving the accuracy of table detection using TableNet.
Citation: SAHUKARA, Krishna Sai. Enhancing TableNet´s Validation: Evaluating the Accuracy of the Existing Architecture with Novel Training Data. České Budějovice, 2023. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta

Institution: University of South Bohemia in České Budějovice (web)
Document availability information: Fulltext is available in the Digital Repository of University of South Bohemia.
Original record: http://www.jcu.cz/vskp/73290

Permalink: http://www.nusl.cz/ntk/nusl-693178


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Universities and colleges > Public universities > University of South Bohemia in České Budějovice
Academic theses (ETDs) > Master’s theses