# Graphical Object Detection in Document Images

arXiv: https://arxiv.org/abs/2008.10843 · HTML: https://mlsanity.com/paper/2008.10843/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 19 of 19)

- [Robust Table Detection and Structure Recognition from Heterogeneous Document Images](https://mlsanity.com/paper/2203.09056/) (2022)
- [DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer](https://mlsanity.com/paper/2201.11438/) (2022)
- [Guided Table Structure Recognition through Anchor Optimization](https://mlsanity.com/paper/2104.10538/) (2021)
- [Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis](https://mlsanity.com/paper/2401.11874/) (2024)
- [SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation](https://mlsanity.com/paper/2305.04609/) (2023)
- [DLAFormer: An End-to-End Transformer For Document Layout Analysis](https://mlsanity.com/paper/2405.11757/) (2024)
- [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)
- [TRACE: Table Reconstruction Aligned to Corner and Edges](https://mlsanity.com/paper/2305.00630/) (2023)
- [A Hybrid Approach for Document Layout Analysis in Document images](https://mlsanity.com/paper/2404.17888/) (2024)
- [Document Intelligence in the Era of Large Language Models: A Survey](https://mlsanity.com/paper/2510.13366/) (2025)
- [Knowledge driven Description Synthesis for Floor Plan Interpretation](https://mlsanity.com/paper/2103.08298/) (2021)
- [GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation](https://mlsanity.com/paper/2402.11401/) (2024)
- [Evaluation of a Region Proposal Architecture for Multi-task Document Layout Analysis](https://mlsanity.com/paper/2106.11797/) (2021)
- [Layout-Aware OCR for Black Digital Archives with Unsupervised Evaluation](https://mlsanity.com/paper/2509.13236/) (2025)
- [TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement](https://mlsanity.com/paper/2312.11043/) (2023)
- [RTHDet: Rotate Table Area and Head Detection in images](https://mlsanity.com/paper/2402.03315/) (2023)
- [EDocNet: Efficient Datasheet Layout Analysis Based on Focus and Global Knowledge Distillation](https://mlsanity.com/paper/2502.16541/) (2025)
- [Complex Layout Classification in the Wild: A Low-Resource Approach with Layout-Preserving Augmentations](https://mlsanity.com/paper/2606.17355/) (2026)

## Abstract (arXiv)

Graphical elements: particularly tables and figures contain a visual summary of the most valuable information contained in a document. Therefore, localization of such graphical objects in the document images is the initial step to understand the content of such graphical objects or document images. In this paper, we present a novel end-to-end trainable deep learning based framework to localize graphical objects in the document images called as Graphical Object Detection (GOD). Our framework is data-driven and does not require any heuristics or meta-data to locate graphical objects in the document images. The GOD explores the concept of transfer learning and domain adaptation to handle scarcity of labeled training images for graphical object detection task in the document images. Performance analysis carried out on the various public benchmark data sets: ICDAR-2013, ICDAR-POD2017,and UNLV shows that our model yields promising results as compared to state-of-the-art techniques.