# TableCenterNet: One-Stage Parallel Regression for Table Structure Recognition

> TableCenterNet is a one-stage end-to-end network that simultaneously regresses both the spatial coordinates and logical row/column indices of table cells in a single forward pass, using interpolation maps to encode the table's logical grid structure.

- Authors: Hunter Heidenreich
- Published: 2026-03-27
- Paper: Preprint (arXiv) <https://arxiv.org/abs/2504.17522>
- Code: TableCenterNet (Code) <https://github.com/dreamy-xay/TableCenterNet>
- Source: Bevaya Labs, <https://labs.bevaya.ai/>

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## TL;DR

TableCenterNet is a one-stage, end-to-end table structure recognition network built on the CenterNet framework. It unifies spatial bounding-box regression and logical row/column index prediction into a single parallel multi-task pass, avoiding the serial two-stage pipelines of prior work. On the TableGraph-24k benchmark, it improves logical location accuracy by 7.2 percentage points over the prior best while running 15.7 times faster than LORE at comparable overall performance.

## What kind of paper is this?

**Dominant:** $\Psi\_{\text{Method}}$: The headline contribution is a new neural network architecture and training recipe. The paper proposes a regression-based approach to jointly predict spatial and logical cell structure from a single model, with ablation studies, SOTA comparisons across three benchmarks, and efficiency analysis all centered on validating the architectural design choices.

## What is the motivation?

Table structure recognition (TSR) requires a system to recover both the physical location of each cell (bounding quadrilateral) and its logical position in the table grid (starting/ending row and column indices). Prior approaches fell into one of two camps, each with significant drawbacks.

The classic two-stage pipeline first detects row and column regions or individual cells, then runs a separate second-stage model to infer logical indices. Methods like TGRNet and LORE are representative: TGRNet uses a Graph Convolutional Network on detected cells, and LORE cascades a Transformer over spatial detections. These require independent training of multiple sub-modules and serial inference, resulting in high latency and compounding prediction errors.

Existing one-stage methods (TableNet, Cycle-CenterNet, SCAN, TRACE) simplified training but each suffered notable limitations in scenario coverage: they either assumed clean, bordered tables or were sensitive to geometric deformations, and none predicted logical indices directly in a single pass without complex post-processing.

The central gap was the absence of a method that could handle diverse real-world table scenarios (borderless, distorted, multi-column spanning, curved) while predicting both spatial and logical structure from a single model with simple training and fast inference.

## What is the novelty?

TableCenterNet extends the CenterNet object detection framework into a multi-task regression architecture. The key idea is to represent the logical structure of the table as a pair of continuous interpolation maps (one for rows, one for columns) that can be regressed directly from features, then use those maps to read off integer row/column indices for any detected cell.

**Overall architecture.** A CNN backbone (DLA-34 or StarNet-s3) produces a shared feature map. Six parallel regression heads decode from that shared representation:

1. A keypoint heatmap for cell corner detection.
2. Keypoint offsets.
3. Two sets of corner-to-center offset vectors $\hat{u}$ (centers-to-corner) and $\hat{v}$ (corners-to-center).
4. A row interpolation map $\hat{I}\_r \in \mathbb{R}^{H \times W}$.
5. A column interpolation map $\hat{I}\_c \in \mathbb{R}^{H \times W}$.
6. Per-cell row/column span regression $\hat{s}\_i = \\{\hat{s}\_i^r, \hat{s}\_i^c\\}$.

**Spatial location regression.** The spatial head follows a modified CenterNet/Cycle-CenterNet approach. Each cell is represented by its four corner points, and the network regresses offset vectors between corners and cell centers. The spatial loss is a weighted combination of L1 losses on these offset vectors, plus a regularization term on invalid (unmatched) corner-to-center vectors:

$$\mathcal{L}\_{\text{spatial}} = \frac{1}{8n} \sum\_{i=1}^{n} \omega(i) \left(\lambda\_u \mathcal{L}\_1(u\_i, \hat{u}\_i) + \lambda\_v \mathcal{L}\_1(v\_i, \hat{v}\_i)\right) + \lambda\_e \mathcal{L}\_e$$

where $\omega(i)$ is a sine-based quality weighting function and $\lambda\_u = 1.0$, $\lambda\_v = 0.5$, $\lambda\_e = 0.2$.

**Interpolation maps and logical location.** The key novelty is how logical indices are obtained without requiring a graph or sequence model. Before training, a Delaunay triangulation-based polygon interpolation algorithm (Algorithm 1) renders a row interpolation map $I\_r$ and column interpolation map $I\_c$ for each training image. Each pixel in $I\_r$ is assigned a value interpolated from the logical row boundaries of the cells overlapping that pixel. Once trained, the network regresses $\hat{I}\_r$ and $\hat{I}\_c$ directly. At inference, the logical indices of cell $i$ are extracted by sampling these maps at the upper-left corner of the predicted cell:

$$\begin{aligned}
\hat{r}\_i^{st} &= \text{round}(\hat{I}\_r[\hat{y}\_{i,1}, \hat{x}\_{i,1}]) \\\\
\hat{r}\_i^{ed} &= \hat{r}\_i^{st} + \lfloor \hat{s}\_r^i \rfloor - 1 \\\\
\hat{c}\_i^{st} &= \text{round}(\hat{I}\_c[\hat{y}\_{i,1}, \hat{x}\_{i,1}]) \\\\
\hat{c}\_i^{ed} &= \hat{c}\_i^{st} + \lfloor \hat{s}\_c^i \rfloor - 1
\end{aligned}$$

**Logical location supervision.** Two auxiliary losses guide the interpolation map regression. The boundary loss $\mathcal{L}\_{\text{boundary}}$ applies higher weight near logical boundaries (integer values in the map) via a squared sine weight function. The span loss $\mathcal{L}\_{\text{span}}$ enforces consistency between the regressed per-cell span $\hat{s}\_i$ and the spans implied by reading the interpolation map at the cell's corners:

$$\mathcal{L}\_{\text{logical}} = \mathcal{L}\_{\text{boundary}} + \mathcal{L}\_{\text{span}}$$

**Selective gridding.** For well-structured datasets without significant deformation, the authors propose decomposing merged (spanning) cells into unit grid cells before generating interpolation maps. This unifies the interpolation style and improves regression accuracy on such datasets.

**Overall loss:**

$$\mathcal{L}\_{\text{overall}} = \mathcal{L}\_{\text{keypoint}} + \mathcal{L}\_{\text{spatial}} + \mathcal{L}\_{\text{logical}}$$

## What experiments were performed?

**Datasets.** Three public benchmarks were used:

- **ICDAR-2013** (156 tables, PDF documents from government websites; modified ICDAR-2013.c version with cell bounding boxes; 80/20 train/test split following prior work).
- **WTW** (Wired Table in the Wild; 10,970 training and 3,611 test images from diverse real-world scenes including curved, occluded, and blurred tables).
- **TableGraph-24k** (20,000 training, 2,000 validation, 2,000 test images of document tables from arXiv papers; includes wired, borderless, and partial-border tables).

**Metrics.** Physical coordinate quality was assessed by precision, recall, and F1 at IoU threshold 0.5 (and 0.9 for WTW). Logical location was assessed by: (1) accuracy of logical location indices, (2) F1 of cell adjacency relations, and (3) TEDS (Tree-Edit-Distance-based Similarity).

**Baselines.** Comparisons were made against TGRNet, LORE, Cycle-CenterNet, SCAN, TRACE, FLAG-Net, NCGM, LGPMA, TabStrNet, TOD, TSRFormer, and CascadeTabNet, covering the main prior paradigms.

**Ablation.** Seven ablation experiments on WTW investigated: (a) whether cell-span regression helps, (b) whether the span-supervised loss $\mathcal{L}\_{\text{span}}$ helps beyond direct span regression, (c) whether the boundary loss $\mathcal{L}\_{\text{boundary}}$ adds value, and (d) whether interpolation map regression is better than regressing logical indices directly from upper-left or all four corners.

**Two model variants.** TableCenterNet-D uses DLA-34 as the backbone; TableCenterNet-S uses the lighter StarNet-s3.

## What are the outcomes/conclusions?

**ICDAR-2013.** TableCenterNet-D improves physical F1 by 0.6 percentage points and logical accuracy by 4.3 percentage points over LORE. TableCenterNet-D and TableCenterNet-S both exceed Cycle-CenterNet and SCAN on adjacency metrics when trained only on WTW (no ICDAR-2013 training data).

**WTW.** At IoU 0.5, both variants match or exceed all prior methods on physical F1 (97.3%). Logical accuracy reaches 83.0% (TableCenterNet-D), versus 95.1% for LORE on adjacency F1 but 82.9% TEDS. TEDS improves 1% over SCAN's 90.7% to reach 91.7%. At the stricter IoU threshold of 0.9, physical F1 (91.3%) improves 13 percentage points over Cycle-CenterNet.

**TableGraph-24k.** TableCenterNet-D achieves 95.1% overall logical accuracy versus 87.9% for LORE (+7.2 pp), with near-parity on physical F1 (96.0% vs. LORE's unreported / TGRNet's 90.6% baseline).

**Efficiency.** Using the same DLA-34 backbone, TableCenterNet-D has 27.3% fewer parameters (16.82 M vs. 27.86 M) and runs 15.7 times faster than LORE (227 ms vs. 3788 ms per image at 1024x1024). TableCenterNet-S (6.27 M parameters, 50.3 GFLOPs, 215 ms) offers further reduction suitable for edge deployment.

**Ablation findings.** Interpolation map regression outperforms direct logical index regression from either one corner (–3.2% Acc) or four corners (–1.2% Acc). The span loss $\mathcal{L}\_{\text{span}}$ contributes the largest single gain (+6.5% Acc on WTW). The boundary loss adds +0.6% Acc. Combined, they account for the majority of the logical accuracy improvement.

**Limitations acknowledged.** The authors note that the WTW dataset focuses on bordered tables, so the method's performance on diverse borderless scenes in the wild is not fully validated. Future work mentions collecting more challenging wireless table data. The paper does not report results on PubTables-1M or other large-scale benchmarks, and comparisons on TableGraph-24k are limited to two prior methods.

## Reproducibility

### Models

- Two backbone variants are evaluated: DLA-34 (18.8 M parameters in LORE; 16.82 M in TableCenterNet-D) and StarNet-s3 (TableCenterNet-S, 6.27 M parameters). Exact layer configurations are not detailed beyond the reference to the original DLA and StarNet papers.
- Six parallel regression heads, each with 256 hidden channels, appended to the backbone's output feature map within the CenterNet regression framework.
- Backbones initialized from ImageNet-pretrained weights.
- Code released under Apache-2.0 at https://github.com/dreamy-xay/TableCenterNet. No information on whether pretrained TSR model weights are released separately.

### Algorithms

- Optimizer: Adam.
- Batch size: 22 for ICDAR-2013 and WTW; 38 for TableGraph-24k.
- Training: 200 epochs. Learning rate starts at $1.25 \times 10^{-4}$, decayed to $1.25 \times 10^{-5}$ at epoch 140 and $1.25 \times 10^{-6}$ at epoch 180.
- Input images resized to 1024x1024 (768x768 for TableGraph-24k), aspect ratio maintained with padding.
- Data augmentation and normalization applied (specific augmentation types not enumerated).
- ICDAR-2013 training used fine-tuning from a WTW-trained model for 100 epochs, with ICDAR-2013 training images border-padded by 100 black pixels on all sides.
- Interpolation map GT generated once before training using Algorithm 1 (Delaunay triangulation-based polygon interpolation).
- Loss hyperparameters: $\lambda\_u = 1.0$, $\lambda\_v = 0.5$, $\lambda\_e = 0.2$.

### Data

- ICDAR-2013.c: modified version with cell bounding boxes rather than word boxes; 258 table images after preprocessing; 80/20 train/test split.
- WTW: 10,970 training / 3,611 test; publicly available; physical and logical coordinates with quadrilateral physical boxes.
- TableGraph-24k: 20,000 train / 2,000 val / 2,000 test; sourced from arXiv scholarly articles.
- All three datasets are publicly available. Licenses not stated in the paper.

### Evaluation

- Physical coordinate metrics: precision, recall, F1 at IoU 0.5 (and additionally IoU 0.9 on WTW).
- Logical location accuracy: per-index accuracy of start/end row/col.
- Adjacency relation F1: follows ICDAR-2013 competition evaluation protocol.
- TEDS: Tree-Edit-Distance-based Similarity between predicted and ground-truth HTML table representations.
- No error bars or confidence intervals are reported. Single-run results throughout.
- Comparisons are not always on identical settings (some baselines use different training data marked with $\ddagger$), which limits direct head-to-head conclusions.

### Hardware

- Training hardware: two NVIDIA GeForce RTX 3090 GPUs (24 GB VRAM each).
- Inference time measured at 1024x1024 input on the WTW test set; TableCenterNet-D: 227.4 ms/image; TableCenterNet-S: 214.8 ms/image. Hardware specification for inference timing is not separately reported (likely the same workstation).
- No cloud compute cost or energy consumption figures are provided.
- The compact TableCenterNet-S (6.27 M parameters, 50.3 GFLOPs) is described by the authors as feasible for edge device deployment.

## BibTeX

```bibtex
@article{xiao2025tablecenternet,
  title={TableCenterNet: A one-stage network for table structure recognition},
  author={Xiao, Anyi and Yang, Cihui},
  journal={arXiv preprint arXiv:2504.17522},
  year={2025}
}
```