ICDAR2017 Competition on Page Object Detection

Abstract

This paper presents the results of ICDAR2017 Competition on Page Object Detection (POD). POD is to detect page objects (tables, mathematical equations, graphics, figures, etc.) from document images. This competition makes use of a dataset consists of 2,000 document page images This dataset contains abundant page objects with various types and layouts. During the competition, we received 13 different teams' registrations and finally 8 of them submitted their results. All teams used deep learning as the basic method, then combined different traditional features or methods to improve the detection performance. The team NLPR-PAL achieved the averaged F1 of 0.898 and mAPs of 0.805 in the detection of all page objects under the IOU threshold 0.8. In this overview paper, we summarize the task design, dataset, results, and the approaches used by those teams of this competitions.