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

arXiv: https://arxiv.org/abs/2008.02569 · HTML: https://mlsanity.com/paper/2008.02569/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Document AI: Benchmarks, Models and Applications](https://mlsanity.com/paper/2111.08609/) (2021)
- [Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction](https://mlsanity.com/paper/2410.21169/) (2024)
- [Robust Table Detection and Structure Recognition from Heterogeneous Document Images](https://mlsanity.com/paper/2203.09056/) (2022)
- [Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks](https://mlsanity.com/paper/2104.14272/) (2021)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer](https://mlsanity.com/paper/2305.02769/) (2023)
- [Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images](https://mlsanity.com/paper/2306.13526/) (2023)
- [Towards End-to-End Semi-Supervised Table Detection with Semantic Aligned Matching Transformer](https://mlsanity.com/paper/2405.00187/) (2024)
- [DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning](https://mlsanity.com/paper/2504.04085/) (2025)
- [Revisiting Table Detection Datasets for Visually Rich Documents](https://mlsanity.com/paper/2305.04833/) (2023)
- [RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization](https://mlsanity.com/paper/2404.09530/) (2024)
- [An extensible point-based method for data chart value detection](https://mlsanity.com/paper/2308.11788/) (2023)
- [Domain Adaptation for Learned Image Compression with Supervised Adapters](https://mlsanity.com/paper/2404.15591/) (2024)
- [SDL: New data generation tools for full-level annotated document layout](https://mlsanity.com/paper/2106.15117/) (2021)
- [CISOL: An Open and Extensible Dataset for Table Structure Recognition in the Construction Industry](https://mlsanity.com/paper/2501.15469/) (2025)
- [EMBLEM: Enhancing Multi-script Table Detection through Masking](https://mlsanity.com/paper/2609.08330/) (2026)

## Abstract (arXiv)

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.