Localization of tables and plots in documents using deep neural networks

Kadiyala, Vishnu Priyatamkumar

Localization of tables and plots in documents using deep neural networks

dc.contributor.advisor	Cheng, Samuel
dc.contributor.author	Kadiyala, Vishnu Priyatamkumar
dc.contributor.committeeMember	Zheng, Bin
dc.contributor.committeeMember	Metcalf, Justin
dc.date.accessioned	2022-05-11T20:27:00Z
dc.date.available	2022-05-11T20:27:00Z
dc.date.issued	2022-05-13
dc.date.manuscript	2022
dc.description.abstract	There has been an immense increase in number of scientific publications being published every single day, it has been increasingly difficult to keep up with all the new results being published. In this research, we localized and detected all the plots and tables from documents using deep neural networks. We generated a custom document dataset and manually annotated it to train and evaluate object detection models and their customizability. We used two Single shot multi detector models with base model of MobileNet, RetinaNet and CenterNet model. We trained these models over 10000 epochs on the custom generated dataset. All three models were able to localize and detect the plots and tables with accurately predicted bounding boxes. The results were as follows with CenterNet having the highest mAP score of 92 and highest AR of 93.88 followed by RetinaNet with mAP score of 91.1 and AR of 93.76 lastly, MobileNet based SSD with mAP score of 89.04 and AR of 91.54.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/335695
dc.language	en_US	en_US
dc.rights	Attribution 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Object Detection	en_US
dc.subject	Custom dataset	en_US
dc.subject	Localization	en_US
dc.subject	Deep Neural Networks	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	Localization of tables and plots in documents using deep neural networks	en_US
ou.group	Gallogly College of Engineering::School of Electrical and Computer Engineering	en_US