# ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition

arXiv: https://arxiv.org/abs/2607.00734 · HTML: https://mlsanity.com/paper/2607.00734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 33 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 29 of 29)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss](https://mlsanity.com/paper/2101.11952/) (2021)
- [Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence](https://mlsanity.com/paper/2106.01883/) (2021)
- [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://mlsanity.com/paper/2110.00061/) (2021)
- [Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection](https://mlsanity.com/paper/2011.12885/) (2020)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://mlsanity.com/paper/2407.17140/) (2024)
- [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://mlsanity.com/paper/2410.13842/) (2024)
- [Optimized Table Tokenization for Table Structure Recognition](https://mlsanity.com/paper/2305.03393/) (2023)
- [ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection](https://mlsanity.com/paper/2303.04989/) (2023)
- [Robust Table Detection and Structure Recognition from Heterogeneous Document Images](https://mlsanity.com/paper/2203.09056/) (2022)
- [GriTS: Grid table similarity metric for table structure recognition](https://mlsanity.com/paper/2203.12555/) (2022)
- [TSRFormer: Table Structure Recognition with Transformers](https://mlsanity.com/paper/2208.04921/) (2022)
- [YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series](https://mlsanity.com/paper/2406.19407/) (2024)
- [Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones](https://mlsanity.com/paper/2103.05959/) (2021)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [Aligning benchmark datasets for table structure recognition](https://mlsanity.com/paper/2303.00716/) (2023)
- [UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition](https://mlsanity.com/paper/2409.13148/) (2024)
- [TFLOP: Table Structure Recognition Framework with Layout Pointer Mechanism](https://mlsanity.com/paper/2501.11800/) (2025)
- [LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training](https://mlsanity.com/paper/2401.01522/) (2024)
- [TABLET: Table Structure Recognition using Encoder-only Transformers](https://mlsanity.com/paper/2506.07015/) (2025)
- [RAPTOR: Refined Approach for Product Table Object Recognition](https://mlsanity.com/paper/2502.14918/) (2025)
- [QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents](https://mlsanity.com/paper/2506.14568/) (2025)

## Abstract (arXiv)

Table Structure Recognition (TSR) aims to recover the row and column layout of tables from document images, a key step in document understanding pipelines. Accurate TSR depends on precise boundary localization: small errors in row or column boundaries can propagate into incorrect cell assignments and structural inconsistencies. Yet detection-based approaches treat table elements as generic objects, ignoring a fundamental property of table layout: rows and columns play structurally distinct roles and their boundaries carry unequal importance. We propose an Edge-constrained Fine-grained Localization loss (EFL) that formalizes this structural asymmetry by encoding table-specific geometric priors into the training objective: row-like elements are supervised with emphasis on their horizontal boundaries, while column-like elements prioritize vertical boundaries. Implemented within a real-time detector with distribution-based boundary refinement (D-FINE), EFL operates during training only and guides boundary refinement toward structurally meaningful adjustments with no change to the inference pipeline. The proposed approach, ConRTF, is also data-efficient, maintaining robust accuracy with as few as 2k--3k annotated tables. Experiments on PubTables-1M and two private datasets show consistent improvements over the optimized baseline and several real-time detectors including RT-DETRv2 and YOLOv10-11, with gains of up to +1.6 GriTS points at equal inference speed.