Layout Analysis on modern Newspapers using the Object Detection model Faster R-CNN
Abstract: As society is becoming more and more digitized the amount of digital data is increasing rapidly. Newspapers are one example of this, that many Libraries around the world are storing as digital images. This enables a great opportunity for research on Newspapers, and a particular research area is Document Layout Analysis where one divides the document into different segments and classifies them. In this thesis modern Newspaper pages, provided by KBLab, were used to investigate how well a Deep Learning model developed for General Object Detection performs in this area. In particular the Faster R-CNN Object detection model was trained on manually annotated newspaper pages from two different Swedish publishers, namely Dagens Nyheter and Aftonbladet. All newspaper pages were taken from editions published between 2010 and 2020, meaning only modern newspapers were considered. The methodology in this thesis involved sampling editions from the given publishers and time periods and then manually annotating these by marking out the desired layout elements with bounding boxes. The classes considered were: headlines, subheadlines, decks, charts/infographics, photographs, pull quotes, cartoons, fact boxes, bylines/credits, captions, tableaus and tables. Given the annotated data, a Faster R-CNN with a ResNet-50-FPN backbone was trained on both the Dagens Nyheter and Aftonbladet train sets and then evaluated on different test set. Results such as a mAP0.5:0.95 of 0.6 were achieved for all classes, while class-wise evaluation indicate precisions around 0.8 for some classes such as tableaus, decks and photographs.
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