Instance Segmentation of Newspaper Elements Using Mask R-CNN
Abdullah M. Almutairi, Meshal Almashan · 2019
Newspaper digitization has gained wide interest around the world. Archives of digitized newspapers contain a wealth of information that spans decades. To extract this abundance of information, optical character recognition (OCR) techniques are used. However, as a first step, the newspaper pages should be logically deconstructed into articles to gain meaningful knowledge. This is difficult due to the complex layout of newspapers and the various styles, shapes, and languages of newspaper articles. Newspaper pages also contain other elements besides articles, such as advertisements that come in multiple shapes and forms, and top headers that contain information about the newspaper's issue and page. Therefore, it is important to detect these elements before information extraction begins. In this paper, we present a deep learning solution for the problem of newspaper page semantic segmentation of the main newspaper elements (articles, advertisements, and page headers). We employed the instance segmentation method mask R-CNN mask_rcnn to create a language-agnostic model that logically deconstructs a newspaper page raw image into its main elements based only on its visual features. We show the results of experiments that display the accuracy and robustness of our model.