  We try to address the problem of document layout understanding using a simple
algorithm which generalizes across multiple domains while training on just few
examples per domain. We approach this problem via supervised object detection
method and propose a methodology to overcome the requirement of large datasets.
We use the concept of transfer learning by pre-training our object detector on
a simple artificial (source) dataset and fine-tuning it on a tiny domain
specific (target) dataset. We show that this methodology works for multiple
domains with training samples as less as 10 documents. We demonstrate the
effect of each component of the methodology in the end result and show the
superiority of this methodology over simple object detectors.
