# TRACE: Table Reconstruction Aligned to Corner and Edges > TRACE is an end-to-end table recognition model that detects cell corners and edges with a single segmentation network and reconstructs the full grid through bottom-up post-processing, eliminating the need for a separate table detection stage. - Authors: Bevaya AI Team - Published: 2026-03-27 - Paper: Preprint (arXiv) - Source: Bevaya Labs, --- ## TL;DR TRACE is a single-model, end-to-end table recognition system that detects corners and edges at the pixel level and reconstructs cell grids from those low-level features without needing a separate table detector. It also introduces SubTableBank, an in-house dataset with per-cell border visibility annotations. At publication, the authors report top results on ICDAR 2013 and WTW. ## What kind of paper is this? **Dominant:** $\Psi\_{\text{Method}}$ (Methodological): The core contribution is a new architecture and post-processing pipeline for bottom-up table reconstruction. The paper provides ablations, baselines, and comparisons across two benchmarks to validate that the corner-and-edge prediction approach works better than prior two-stage pipelines. **Secondary:** $\Psi\_{\text{Resource}}$ (Infrastructure): The authors describe an in-house dataset, SubTableBank, with annotated cell bounding boxes and per-edge visibility flags. A portion is promised for public release, though no public link is provided in this preprint. ## What is the motivation? Table recognition in document images is typically split into two sequential steps: table detection (TD) to localize the table region, and table structure recognition (TSR) to identify rows, columns, and spanning cells. Running two separate models is expensive at training and inference time, and errors from the detection stage propagate into structure recognition with no feedback between the two. The authors argue that the field has studied end-to-end approaches only sparsely, and that the few examples that exist still require parallel branches for bordered versus borderless tables, or rely on OCR input to handle empty cells. The deeper motivation is conceptual: a table is a hierarchical object built from cells, and each cell is a rectangle defined by its four corners and four edges. If a model can detect those fundamental visual elements directly, cells and tables can be assembled bottom-up through simple post-processing, without ever needing to detect the high-level bounding box first. ## What is the novelty? The central contribution is the framing of table reconstruction as low-level feature prediction rather than object detection. A single U-Net style CNN with a ResNet-50 backbone outputs five segmentation maps: - one corner heatmap (Gaussian blobs at every cell corner) - two explicit edge maps (visible horizontal and vertical borders) - two implicit edge maps (invisible horizontal and vertical separators) The distinction between explicit and implicit edges is important for borderless tables. Previous split-merge methods either ignore invisible lines altogether or require separate model branches. TRACE instead labels every cell border by visibility at annotation time and predicts both types jointly in a shared network. The training objective is a pixel-wise MSE loss across all five channels: $$ L = \sum\_{p}\sum\_{i} \| S\_{i}(p) - S\_{i}^{\ast}(p) \|^{2}\_{2} $$ where $S\_{i}(p)$ is the ground truth for the $i$-th map at pixel $p$, and $S\_{i}^{\ast}(p)$ is the model prediction. The post-processing pipeline reconstructs tables through a split-merge strategy. Explicit and implicit edge maps are binarized and projected onto the horizontal and vertical axes. The midpoint of each projected edge cluster becomes a separator. For implicit horizontal lines where rows contain empty cells, ambiguity in the projection is resolved using corner map peak positions. Spanning cells are recovered by checking whether an edge is present at the midpoint between adjacent cell candidates; if not, they are merged. Table location is finally derived from the bounding coordinates of all reconstructed cells. An optional image rectification step applies the Hough line transform followed by a homography to correct perspective distortion before reconstruction. This step is mostly relevant for scene-image tables in the WTW dataset. ## What experiments were performed? The authors evaluate on two public benchmarks. **ICDAR 2013 Table Competition** contains 156 PDF-rendered document tables from EU/US government websites. The official evaluation uses character-level recall, precision, and F1 for table detection, along with Purity and Completeness scores. Table structure recognition is evaluated with adjacency-relation recall and precision. TRACE is tested without any fine-tuning on ICDAR 2013 data. **Wired Table in the Wild (WTW)** includes 10,970 training and 3,611 test images of tables captured in natural scenes with perspective distortion, rotation, and varied lighting. Detection is measured at cell level with IoU = 0.9. Structure recognition is measured by cell adjacency relation recall and precision using IoU = 0.6 for cell matching. **Baselines:** For ICDAR 2013, comparisons include Nurminen (PDF-based), TableNet, DeepDeSRT, GTE, SPLERGE, LGPMA, and GTE with ground-truth table crops. For WTW, comparisons include Cycle-CenterNet (TD+TSR), TSRFormer (TSR-only with GT table), and NCGM (TSR-only with GT table). **Training setup:** ResNet-50 pretrained on ImageNet, input resized to 1280 on the long side, Adam optimizer, initial learning rate $1 \times 10^{-4}$ decayed every 10k steps, 100k total iterations, batch size 12, Online Hard Negative Mining, and standard augmentations. The in-house dataset of 9,717 images (7,783 train / 971 val / 963 test) is used for training. ## What are the outcomes/conclusions? On ICDAR 2013 table detection, TRACE reaches F1 = 97.53%, with the highest Completeness (150) and Purity (147) scores, outperforming all image-based methods on completeness and purity even though GTE leads on raw F1. On ICDAR 2013 structure recognition, TRACE reaches F1 = 97.46% end-to-end, the highest reported result and the only method evaluated without pre-cropped GT table regions. Methods with GT crops score lower (GTE with GT at 96.24%). On WTW structure recognition, TRACE achieves F1 = 94.5%, which is the best among compared methods, again without requiring GT table regions. Cell-level detection at IoU = 0.9 is 64.8% F1, which the authors note is inflated downward by annotation imprecision on small cells; at IoU = 0.7 the same model scores 94.9% F1. **Bordered vs. borderless breakdown (ICDAR 2013):** TSR F1 on bordered tables is 99.4%, while borderless tables score 93.85%. The primary failure modes involve ambiguous implicit edge placement, mixed explicit-implicit separators along the same line, and partial table detection when cell content spacing is large. **Limitations acknowledged:** - Implicit edge prediction is the main source of errors, especially when content gaps are large or mixed-type separators appear. - The model uses a CNN backbone (ResNet-50) without global attention; the authors suggest Swin Transformer or similar architectures as future work. - Evaluation on tag-generation benchmarks (SciTSR, PubTabNet, TableBank) is out of scope because TRACE does not produce text content and those metrics require OCR. - The internal dataset used for training is not yet publicly available at the time of publication. ## Reproducibility ### Models - Architecture: U-Net style encoder-decoder, ResNet-50 backbone, five-channel segmentation head (one corner map, four edge maps). - No pretrained TRACE weights are released. - Backbone initialized from ImageNet-pretrained ResNet-50. ### Algorithms - Optimizer: Adam. - Learning rate: $1 \times 10^{-4}$, decayed every 10k iterations. - Total training: 100k iterations, batch size 12. - Online Hard Negative Mining applied during training. - Augmentations: color jitter, random rotations, random cropping. - Binarization thresholds: 0.5 for explicit edge maps, 0.2 for implicit edge maps. - Edge length filter: edges shorter than 25% of the corresponding table dimension are discarded before the split step. - No fine-tuning performed on ICDAR 2013; the model trained on the in-house dataset is applied directly. ### Data - In-house training dataset: 9,717 annotated document images (financial and scientific documents plus some from TableBank). Split: 7,783 train / 971 val / 963 test. - Annotations include cell bounding boxes with per-edge visibility flags (explicit vs. implicit). - The dataset is described as "SubTableBank." The authors state they "will soon make a portion" available publicly; no URL is provided in this preprint. - WTW dataset (public): 10,970 train / 3,611 test; wired tables only from natural scenes. Source: Long et al., ICCV 2021. - ICDAR 2013 benchmark: 156 tables from EU/US government PDFs; used for testing only. ### Evaluation - ICDAR 2013 TD: character-level recall, precision, F1, Purity, Completeness (official protocol). - ICDAR 2013 TSR: adjacency-relation recall and precision. - WTW: cell-level recall/precision/F1 at IoU = 0.9 for detection; adjacency-relation recall/precision at IoU = 0.6 for structure. - The paper notes that IoU = 0.9 for cell detection is strict relative to annotation quality, and IoU = 0.7 may be more appropriate for small cells. Additional results at IoU 0.8, 0.7, and 0.6 are provided. - No error bars or multiple-run statistics are reported. ### Hardware - Training hardware not specified. - No inference latency or throughput numbers are reported. - No cost or energy estimates provided. ## BibTeX ```bibtex @inproceedings{baek2023trace, title={TRACE: Table Reconstruction Aligned to Corner and Edges}, author={Baek, Youngmin and Nam, Daehyun and Surh, Jaeheung and Shin, Seung and Kim, Seonghyeon}, booktitle={International Conference on Document Analysis and Recognition}, year={2023} } ```