IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents

TL;DR

The paper introduces IIIT-AR-13K, a large, diverse dataset of annotated business document images for graphical object detection, and benchmarks it with state-of-the-art models to establish baselines.

Contribution

It provides the largest manually annotated dataset for graphical objects in business documents, enabling improved detection methods and benchmarking.

Findings

High baseline performance with Faster R-CNN and Mask R-CNN

Demonstrates dataset's effectiveness for training graphical object detectors

Single model trained on this dataset outperforms larger data-trained models

Abstract

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.