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REVIEW 4 major objections 6 minor 42 references

Uncertainty-Aware Complex Scientific Table Data Extraction

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read An uncertainty-aware pipeline for scientific tables flags likely OCR and structure errors, so verifying just 47% of extracted cells raises data accuracy by about 30%.

desk verdict Interesting idea, but the headline verification savings are in-sample because the flagging threshold was tuned on the test set. read the letter →

arxiv 2507.02009 v2 pith:FL7PQ3OY submitted 2025-07-02 cs.IR

Kehinde Ajayi , Yi He , Jian Wu This is my paper · ORCID
classification cs.IR
keywords tablestructurerecognitionopticalcharacteruncertaintyquantificationconformalpredictionscientificextractionadaptivesetshuman-in-the-loopdataquality
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that uncertainty quantification can make human-in-the-loop extraction from complex scientific tables practical. It builds a TSR-OCR pipeline and wraps it in conformal prediction, giving each extracted cell an uncertainty score; cells above a threshold are flagged for review. On GenTSR and ICDAR-2013 tables, it reports that reviewing only 47% of cells improves data accuracy by about 30% (from roughly 53% to 83%), and cuts verification work by 53%. A reader should care because scientific table extraction currently demands full manual verification to be usable; this is a concrete, model-agnostic way to target verification where errors actually are.

What carries the argument

The central object is the conformal score assigned to each constructed grid cell. TSR row and column boxes are intersected to form grid cells; OCR text boxes are matched to cells by intersection-over-area exceeding 50%; each cell inherits a TSR confidence (average of row and column confidences) and an OCR confidence (average of matched text-box confidences). Conformal prediction on a calibration half of the cells yields a threshold q-hat, and each cell receives uncertainty U(X)=max(0, s(X)-q-hat). The paper compares three score functions: LAC (minimum of TSR and OCR confidence), APS (cumulative confidence), and a hybrid spatial score, with APS giving the best F1 and becoming the primary choice.

What would settle it

Run the pipeline with the flagging threshold selected on a separate validation split or via nested cross-validation, then count the fraction of cells requiring human review and the post-correction accuracy; if the 47%, 53%, and 30% figures degrade materially, the headline savings are an artifact of threshold selection on the evaluation set.

Watch

Extended reading notes

Core claim

The central claim is that conformal prediction applied to combined TSR and OCR confidences can reliably flag incorrectly extracted cells in scientific tables. On the paper's own terms, with the APS score the pipeline flags most errors (overall recall about 65%) while keeping most flagged cells genuinely wrong (overall precision about 70%), and if a human corrects only the flagged cells, overall data accuracy improves by about 30%, with Computer Science tables improving from 53% to 97%. The framework is model-agnostic: any TSR or OCR model that outputs confidence scores can be plugged in, and the uncertainty score is produced by comparing a conformal score against a calibration-set threshold.

Load-bearing premise

The reported verification savings and quality gains assume the flagging threshold can be chosen on the same data used to measure success; if the threshold must be fixed on separate validation data, the 47% and 30% figures would likely change.

Editorial extensions

If this is right

  • If the UQ signal is trusted, extraction pipelines can stop verifying every cell and instead route only flagged cells to a human reviewer, preserving most of the accuracy gain at half the labor cost.
  • Because the framework only requires confidence outputs from the underlying models, replacing the OCR or TSR engine with a better one should directly reduce both true errors and false flags without re-engineering the UQ layer.
  • APS is the recommended score function among those tested; OCR-only confidence is close but slightly worse, and TSR-only confidence is clearly worse, so both structure and text uncertainty matter for error detection.
  • UQ-informed correction works unevenly across domains: tables with clean structure and short text (Computer Science) benefit most, while Biology and Materials Science retain more errors due to symbols and long content, pointing to a need for domain-adaptive scoring.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: because the 0.03 flagging threshold was selected on the evaluation data itself by maximizing F1, the headline 47% and 30% figures likely overstate out-of-sample performance; a validation-set or nested threshold selection would give a fairer estimate of the savings.
  • Editorial inference: the same per-cell uncertainty scores could be used to order the human review queue rather than binary flagging, so a reviewer with a fixed time budget could stop after the highest-uncertainty cells and still capture most errors.
  • Editorial inference: the grid-cell representation with uncertainty attached could feed downstream database loading or scientific knowledge bases by marking low-confidence cells as requiring review instead of silently inserting potentially wrong values.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes TSR-OCR-UQ, a pipeline that integrates table structure recognition (TATR) and OCR (PaddleOCR) with uncertainty scores derived from conformal-prediction-style calibration, in order to flag likely-incorrect extracted cells for human verification. The method is evaluated on the GenTSR and ICDAR-2013 datasets, with several scoring functions (LAC, APS, HSS) compared. The central claim is that by manually verifying only 47% of extracted cells, the data accuracy improves by about 30% (from 53% to 83%), and the verification effort is reduced by 53%. The paper also reports domain-wise precision/recall/F1 for error flagging and includes a discussion of failure modes. Code and data are publicly available.

Significance. If the quantitative claims were established, this would be a practically useful contribution to scientific table extraction, since it would provide a way to prioritize human verification effort using uncertainty information. The paper is also valuable for comparing several uncertainty scoring functions and for releasing an open implementation of the pipeline. However, the headline numbers are currently supported only by an evaluation procedure that selects key thresholds on the test set and lacks a random-verification control, and the connection to conformal prediction's formal coverage guarantee is not established as written. These issues are central, not cosmetic, so the paper needs a substantive revision before the main claims can be accepted.

major comments (4)
  1. [Section 4.1, Table 3] The APS flagging threshold τ=0.03 is selected by maximizing the F1-score on 'our dataset' (Section 4.1), and the same data is then used to report Table 3 and the headline 47%/30% figures in Section 4.3. Since the labor savings in Eq. (15) is a direct function of the number of flagged cells, selecting τ on the evaluation set makes the reported labor savings and accuracy gains in-sample optima. The paper must split the data into calibration, validation, and test (or use nested cross-validation), select τ on a validation set, and then report the metrics on a held-out test set. The sensitivity of the 47% and 30% numbers to τ should also be reported.
  2. [Section 4.3.2, Section 4.3.3] No random-verification baseline is reported. Correcting a randomly chosen 47% of the cells would be expected to remove roughly 47% of the existing errors, assuming errors are not concentrated. The paper should compare UQ-informed correction against this random baseline (repeated over multiple random seeds) to demonstrate that the flagged cells are enriched for errors. Without this comparison, the 30% accuracy improvement cannot be attributed to the uncertainty scoring rather than to the mere act of verifying a fraction of the cells.
  3. [Section 3.3.1, Eq. (5), Eq. (6), Eq. (10)] The scores s_LAC and s_APS are defined using the confidence of the predicted cell Y, not the true label. In conformal prediction, the conformity score must be a function of the true label (e.g., 1 - f(X)_Y for LAC) for the quantile in Eq. (3) to yield the coverage guarantee stated in Eq. (2). As written, the scores are label-independent functions of the model's confidence for its own prediction, so the procedure is a heuristic calibration of confidence scores rather than conformal prediction, and the coverage guarantee does not follow. The authors should either reformulate the scores to depend on the true label during calibration or remove the claim of conformal prediction and describe the method as a calibrated uncertainty-scoring heuristic.
  4. [Section 3.4, Eq. (2), Eq. (3)] The calibration set is described as 'a randomly selected 50% subset of the extracted cells from each domain,' and it is not stated whether the test tables listed in Section 4.2 are disjoint from these calibration cells. If calibration and test cells come from the same tables, within-table correlation can inflate the apparent flagging performance and can break the exchangeability assumption needed for the conformal guarantee. The paper should use table-level disjoint splits for calibration and testing, and should also verify and report the empirical coverage of the prediction sets at the chosen α level, which is currently missing.
minor comments (6)
  1. [Abstract and Section 4.3.3] The '30% improvement' is in absolute percentage points (from 53% to 83%); please state this explicitly to avoid confusion with a relative improvement.
  2. [Section 3.3.1, Eq. (8)] The HSS weight parameters w_row, w_col, and w_text are tuned by a grid search; please state whether this tuning is performed on the calibration set or on the test set, since tuning on the test set would inflate the HSS results.
  3. [Section 3.2.3] The treatment of cells with no OCR match (OCR confidence set to 0) should be justified, because blank cells may be correctly empty and setting confidence to 0 may cause systematic false flagging of legitimate blank cells.
  4. [Table 2 vs Table 3] Table 2 reports an APS mean F1-score of 0.761 while Table 3 reports an ALL-domain F1 of 0.590; please clarify whether these are computed over different units (e.g., tables vs cells, or with vs without threshold selection) so that readers can reconcile the two numbers.
  5. [Equation (1)] The Levenshtein accuracy can become negative when the Levenshtein distance exceeds the maximum string length; clipping the value to [0,1] would make the metric more interpretable.
  6. [References] There is a typo in reference [39], which reads 'ICDAR-2103' and should be 'ICDAR-2013'.

Circularity Check

1 steps flagged · score 6.0 of 10

The 47%/30% headline is an in-sample threshold-selection result: Sec. 4.1 chooses τ=0.03 by maximizing F1 on the same data used to report the labor savings and quality gain.

  1. fitted input called prediction [Sec. 4.1 (Conformal Score Function Selection) and Sec. 4.3.2/4.3.3 (reported headline results)]
    "To evaluate the effectiveness of different score functions, we tested a range of thresholds (0.01 - 1) for each function and identified the optimal threshold (0.03) for APS score function that maximized the F1-score for our dataset. ... In addition, UQ improves efficiency, with an overall labor savings of 53%, meaning that only 47% of extracted cells require manual review."

    The flagging threshold τ=0.03 is selected on the same evaluation set that later produces the headline metrics. Sec. 4.1 states the threshold was chosen to maximize F1 'for our dataset'; Sec. 4.3.2 then reports precision, recall, F1, and labor savings on that same data, and Sec. 4.3.3 reports the ~30% accuracy improvement after correcting those flagged cells. Because τ is an extra free parameter beyond the CP calibration quantile q (Eq. 10), the reported 47%/30% results are optima of an in-sample search rather than out-of-sample predictions. No separate validation split or random-verification control is described, so the central quantitative claim is not shown to be a property of the UQ method rather than of the selected threshold.

full rationale

The paper's core methodological contribution—integrating TSR and OCR and applying conformal-prediction-style uncertainty scores—is not itself circular: the framework is evaluated against external ground truth, and the conformal calibration step uses a held-out 50% subset of cells. However, the headline quantitative claims (53% labor savings, 30% quality improvement) depend on an additional flagging threshold τ=0.03 selected by maximizing F1 on the very evaluation set used to report those numbers (Sec. 4.1 vs. Sec. 4.3). This makes the central 'prediction' an in-sample fitted optimum rather than an out-of-sample estimate. The GenTSR dataset citation [18] is a self-citation (Ajayi, Choudhury, Rajtmajer, Wu) but it is a dataset construction artifact, not a load-bearing circular derivation; TATR's reported benchmarks are external. So the partial circularity is concentrated in the threshold-selection/evaluation loop, justifying a score of 6 rather than full self-definitional circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The main free parameter is the flagging threshold fitted to the test set; the HSS weights are also fitted. No new conceptual entities are introduced.

free parameters (2)
  • flagging threshold (APS) = 0.03
    Selected to maximize F1-score on the evaluation test set in Section 4.1; the headline labor savings and accuracy gains depend on this in-sample choice.
  • HSS weights w_row, w_col, w_text = not reported
    Optimized by grid search to minimize prediction set size while maintaining target coverage (Section 3.3.1); actual values are not given in the paper.
assumptions (4)
  • domain assumption Calibration and test cells are exchangeable within each domain
    Conformal prediction coverage (Eq. 2-4) requires exchangeability; here the 50% calibration split is drawn randomly from the same tables used for evaluation, so exchangeability holds only for cells from those specific tables, not for new tables.
  • domain assumption Softmax confidence scores from TATR and PaddleOCR are meaningful uncertainty signals
    The conformal scores (LAC, APS, HSS) are built directly on these confidences (Eq. 5-9). If detector/OCR confidences are miscalibrated, the uncertainty scores will not correlate with correctness.
  • domain assumption IoA greater than 50% correctly maps OCR bboxes to grid cells
    Section 3.2.3 uses a fixed 50% intersection-over-area threshold for matching; this choice is not validated and affects the cell confidence aggregation.
  • domain assumption Human correction of flagged cells is perfect
    The 'After Human Correction' phase (Section 4.2, Eq. 16) replaces flagged cells with ground truth, emulating a perfect human; in practice correction introduces errors and costs.

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Cite this review

Pith. "Pith review of Uncertainty-Aware Complex Scientific Table Data Extraction." pith.science (2026). https://pith.science/paper/FL7PQ3OY

@misc{pith2026250702009,
  author       = {Pith},
  title        = {Pith review of: Uncertainty-Aware Complex Scientific Table Data Extraction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FL7PQ3OY}},
  note         = {Machine review of arXiv:2507.02009}
}
read the original abstract

Table structure recognition (TSR) and optical character recognition (OCR) play crucial roles in extracting structured data from tables in scientific documents. However, existing extraction frameworks built on top of TSR and OCR methods often fail to quantify the uncertainties of extracted results. To obtain highly accurate data for scientific domains, all extracted data must be manually verified, which can be time-consuming and labor-intensive. We propose a framework that performs uncertainty-aware data extraction for complex scientific tables, built on conformal prediction, a model-agnostic method for uncertainty quantification (UQ). We explored various uncertainty scoring methods to aggregate the uncertainties introduced by TSR and OCR. We rigorously evaluated the framework using a standard benchmark and an in-house dataset consisting of complex scientific tables in six scientific domains. The results demonstrate the effectiveness of using UQ for extraction error detection, and by manually verifying only 47% of extraction results, the data quality can be improved by 30%. Our work quantitatively demonstrates the role of UQ with the potential of improving the efficiency in the human-machine cooperation process to obtain scientifically usable data from complex tables in scientific documents. All code and data are available on GitHub at https://github.com/lamps-lab/TSR-OCR-UQ/tree/main.

Figures

Figures reproduced from arXiv: 2507.02009 by the authors.

Figure 1
Figure 1. (a) and (b) represent the results of OCR and TSR respectively, and (c) represents the results after they are [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A schematic illustration of the proposed UQ pipeline ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Impact of UQ and human correction emulation on table extraction. Red boxes indicate cells whose coordinates [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Data accuracy (left)and Error rate (right) before UQ and after human correction across tables in 4 domains. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

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