Rethinking detection based table structure recognition for visually rich document images
Bibliographic record
Abstract
Detection models have been extensively employed for the Table Structure Recognition (TSR) task, aiming to convert table images into structured formats by detecting table components such as Columns and Rows. However, prevailing detection-based TSR models usually cannot perform well regarding cell-level metrics, such as TEDS, and the reasons hindering their performance are not thoroughly explored. Therefore, we first examine the underlying reasons impeding these models’ performance and find that the key issues are the improper problem formulation, the mismatch issue of detection and TSR metrics, the inherent characteristics of detection models, and the influence of local and long-range feature extraction. Based on these findings, we propose a tailored Cascade R-CNN based solution by introducing a new problem formulation, tuning the proposal generation, and applying deformation convolution and the proposed Spatial Attention Module. The experimental results show that our proposed model can improve the base Cascade R-CNN model by 19.32%, 11.56%, and 14.77% on the SciTSR, FinTabNet, and PubTables1M datasets regarding the structure-only TEDS, achieving state-of-the-art performance, demonstrating that our findings can serve as a valuable guide for enhancing detection-based TSR models. Our code and pre-trained models are public available 1 1 https://github.com/uobinxiao/CascadeTSRDet . . • Comprehensive analysis of the reasons impeding existing detection-based Table Structure Recognition (TSR) solutions. • Proposing a simple and effective state-of-the-art detection-based TSR solution. • Providing a general design guidance for all detection-based TSR solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".