  Publications are an integral part in a scientific community. Bibliographic
reference extraction from scientific publication is a challenging task due to
diversity in referencing styles and document layout. Existing methods perform
sufficiently on one dataset however, applying these solutions to a different
dataset proves to be challenging. Therefore, a generic solution was anticipated
which could overcome the limitations of the previous approaches. The
contribution of this paper is three-fold. First, it presents a novel approach
called DeepBiRD which is inspired by human visual perception and exploits
layout features to identify individual references in a scientific publication.
Second, we release a large dataset for image-based reference detection with
2401 scans containing 38863 references, all manually annotated for individual
reference. Third, we present a unified and highly configurable end-to-end
automatic bibliographic reference extraction framework called BRExSys which
employs DeepBiRD along with state-of-the-art text-based models to detect and
visualize references from a bibliographic document. Our proposed approach
pre-processes the images in which a hybrid representation is obtained by
processing the given image using different computer vision techniques. Then, it
performs layout driven reference detection using Mask R-CNN on a given
scientific publication. DeepBiRD was evaluated on two different datasets to
demonstrate the generalization of this approach. The proposed system achieved
an AP50 of 98.56% on our dataset. DeepBiRD significantly outperformed the
current state-of-the-art approach on their dataset. Therefore, suggesting that
DeepBiRD is significantly superior in performance, generalized, and independent
of any domain or referencing style.
