  We present an approach for adapting convolutional neural networks for object
recognition and classification to scientific literature layout detection
(SLLD), a shared subtask of several information extraction problems. Scientific
publications contain multiple types of information sought by researchers in
various disciplines, organized into an abstract, bibliography, and sections
documenting related work, experimental methods, and results; however, there is
no effective way to extract this information due to their diverse layout. In
this paper, we present a novel approach to developing an end-to-end learning
framework to segment and classify major regions of a scientific document. We
consider scientific document layout analysis as an object detection task over
digital images, without any additional text features that need to be added into
the network during the training process. Our technical objective is to
implement transfer learning via fine-tuning of pre-trained networks and thereby
demonstrate that this deep learning architecture is suitable for tasks that
lack very large document corpora for training ab initio. As part of the
experimental test bed for empirical evaluation of this approach, we created a
merged multi-corpus data set for scientific publication layout detection tasks.
Our results show good improvement with fine-tuning of a pre-trained base
network using this merged data set, compared to the baseline convolutional
neural network architecture.
