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

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. By contrast, we employed four state-of-the art object detection models and compared their performance with state-of-the-art text based reference extraction models. Evaluations are performed on the 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.