  We introduce a new dataset for graphical object detection in business
documents, more specifically annual reports. This dataset, IIIT-AR-13k, is
created by manually annotating the bounding boxes of graphical or page objects
in publicly available annual reports. This dataset contains a total of 13k
annotated page images with objects in five different popular categories -
table, figure, natural image, logo, and signature. It is the largest manually
annotated dataset for graphical object detection. Annual reports created in
multiple languages for several years from various companies bring high
diversity into this dataset. We benchmark IIIT-AR-13K dataset with two state of
the art graphical object detection techniques using Faster R-CNN [20] and Mask
R-CNN [11] and establish high baselines for further research. Our dataset is
highly effective as training data for developing practical solutions for
graphical object detection in both business documents and technical articles.
By training with IIIT-AR-13K, we demonstrate the feasibility of a single
solution that can report superior performance compared to the equivalent ones
trained with a much larger amount of data, for table detection. We hope that
our dataset helps in advancing the research for detecting various types of
graphical objects in business documents.
