Benchmarking Object Detection Networks for Image based Reference Detection in Document Images

Syed Tahseen Raza Rizvi, Adriano Lucieri, Andreas Dengel, Sheraz Ahmed

In: IEEE (editor). International Conference on Digital Image Computing: Techniques and Applications (DICTA). International Conference on Digital Image Computing Techniques and Applications (DICTA-2019) 21st December 2-4 Perth WA Australia IEEE 2019.


In this paper we study the performance evaluation of state-of-the-art object detection models for the task of bibliographic reference detection from document images. The motivation of evaluating object detection models for the task in hand is inspired from how human perceive a document containing bibliographic references. Humans can easily distinguish between different references just by exploiting the layout with a glimpse of an eye, without understanding the content. Existing state-of-the-art systems for bibliographic reference detection are purely based on textual content. Contrary to this, we employed 4 state-of-the-art object detection models and compared their performance with state-of-the-art text based reference extraction. Evaluations are performed on a publicly available dataset (ICONIP) for image based reference detection, containing 455 scanned bibliographic documents with 8766 references from Social Sciences books and journals. Evaluation results reveal the superiority of image based methods for the task of reference detection in document images.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz