Logical Layout Analysis using Deep Learning

Abstract

Logical layout analysis plays an important part in document understanding. It can become a challenging task due to varying formats and layouts. Researchers have proposed different ways to solve this problem, mostly using visual information in some way and a complex pipeline. In this paper, we present a simple technique for labelling the logical structures in document images. We use visual and textual features from the document images to label zones. We utilize Recurrent Neural Networks, specifically 2 layers of LSTM, which input the text from the zone that we want to classify as sequences of words and the normalized position of each word with respect to the page width and height. Comparisons are made by comparing the image under test with the known layouts and labels are assigned to zones accordingly. The labels are abstract, title, author names, and affiliation; however, the text also contains very important information for the task at hand. The presented approach achieved an overall accuracy of 96.21% on publicly available MARG dataset.