Automated High-Precision Extraction and Forensic Verification of Data-Bearing Vector Figures

• URL: http://arxiv.org/abs/2606.31345v1
• Date: Tue, 30 Jun 2026 08:43:32 GMT
• Title: Automated High-Precision Extraction and Forensic Verification of Data-Bearing Vector Figures
• Abstract summary: The quantitative record of science and engineering is increasingly carried by figures rather than text or tables.<n>When a figure is stored as graphics, its data are not approximated by the picture but encoded in it.<n>We turn this into three contributions, one per shortcoming of hand digitization.
• Score: 45.39546965981896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quantitative record of science and engineering is increasingly carried by figures rather than text or tables, and a reader who needs the underlying numbers must usually re-digitize them by hand: slowly, imprecisely, and with no way to prove the result is faithful. Yet when a figure is stored as vector graphics, its data are not approximated by the picture but encoded in it: the renderer writes each marker and vertex at a printed precision that, for the dominant scientific toolchain, exceeds the data's own. We turn this into three contributions, one per shortcoming of hand digitization. First, a precision theory bounding how accurately data can be recovered for a given renderer and export format: bit-exact float32 for matplotlib markers, and a calibration-limited three to four significant figures end to end. Second, an automatic extractor that decodes a figure in one pass with no human in the loop, in place of the slow point-by-point tracing a digitizer demands. Third, a verification theory: recovery is injective except on a characterized, vanishingly small interval near zero; accidental agreement between unrelated data is astronomically unlikely; and a re-rendering certificate binds the recovered values to the markers, lines, and ticks the figure draws, not its text, making a result non-repudiable. With no ground truth used during recovery, decoded figures match external archives (Planck 2018 to 10^-9; the Keeling CO2 record to 5*10^-4, and one decoded figure independently reproduces a correction to the Chinchilla scaling-law confidence interval. We map the achievable precision across common renderers and their PDF, SVG, and EPS formats. What we deliver is certified data; the scientific significance of any particular dataset lies outside this paper's scope, and recovered values are candidates for human review, never accusations.

Related papers

• Same Problem, Different Field: Cross-Domain Solution Import via Domain-Stripped Computational Fingerprints [0.0]
  We distill each paper once into a domain- and method-name-stripped faceted computational fingerprint.<n>The goal is solution import: surface cross-field pairs solving the same problem, so a bespoke implementation can be swapped for another field's standard, specialized solver.<n>On a benchmark of 18 method families across 109 papers, the skeleton lifts cross-domain retrieval average precision over the abstract from 0.222 to 0.513, and the whole fingerprint reaches 0.557.
  arXiv  Detail & Related papers  (2026-09-07T15:06:22Z)
• When Does Consensus Mean Correctness? Measuring the Agreement-Accuracy Coupling with Semantics-Preserving Re-Rendering [0.8594140167290097]
  A model's agreement across perturbed inputs is used as a label-free reliability signal.<n>Rerendering beats resampling on both accuracy and reliability.<n>Fine-tuning on the model's own cross-render consensus inverts.
  arXiv  Detail & Related papers  (2026-08-06T07:08:05Z)
• Out-of-Length Scene Text Recognition: A Two-Axis Diagnosis and a Training-Free Fix [0.3437656066916039]
  Scene Text Recognition models are trained almost exclusively on word crops of at most 25 characters.<n>This paper diagnoses that failure and then closes it.<n>We release the diagnosis harness and implementation.
  arXiv  Detail & Related papers  (2026-07-25T13:16:57Z)
• PosterHarness: Turning Scientific Poster Generation into an Auditable Instruction-Following Benchmark [7.957250777212916]
  Text-rich image models can now design poster-scale layouts, but we lack ways to measure whether they honor scientific communication contracts.<n>We present POSTERHARNESS, an auditable harness reframing poster generation as measurable instruction-following tasks.
  arXiv  Detail & Related papers  (2026-07-03T06:39:56Z)
• PointSplat: Compact Gaussian Splatting via Human-Centric Prediction [70.45838533838104]
  We propose PointSplat, a novel human-centric approach to producing 3D human representations from input views on the fly.<n>The proposed method first estimates a coarse geometric proxy and performs ray casting to prune redundant points.<n>It then employs a Point-Image Transformer to fuse appearance and geometry features, predicting Gaussian attributes in a single forward pass.
  arXiv  Detail & Related papers  (2026-06-30T17:59:05Z)
• ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading [0.1527216171684335]
  We release ConnectomeBench2, a unified multi-species dataset of over 716,485 expert-labeled proofreading decisions.<n>A single Vision Transformer with shared encoders for mesh geometry and electron microscopy reaches human-level accuracy across species for split error correction and merge error identification.<n>The benchmark provides the infrastructure to train and evaluate increasingly capable vision models for connectomic proofreading.
  arXiv  Detail & Related papers  (2026-06-19T05:37:42Z)
• When Probing Accuracy Saturates, Fragility Resolves: A Complementary Metric for LLM Pre-Training Analysis [1.0829694003408499]
  Linear probing declares a property "encoded" when a classifier on hidden states achieves high accuracy.<n>Standard linear probing saturates within the first few thousand steps, leaving most of training invisible to the instrument.<n>We introduce fragility, a complementary per-layer metric defined as the activation-noise level at which probe accuracy collapses.
  arXiv  Detail & Related papers  (2026-06-09T19:00:23Z)
• GRAD: Graph-Retrieved Adaptive Decoding for Hallucination Mitigation [12.69955054591315]
  We introduce Graph-Retrieved Adaptive Decoding (GRAD), a decoding-time method that grounds generation in corpus-derived evidence without retraining.<n>Across three models and a range of question-answering benchmarks, GRAD consistently surpasses baselines.<n>GRAD offers a lightweight, plug-and-play alternative to contrastive decoding and knowledge graph augmentation.
  arXiv  Detail & Related papers  (2025-11-05T22:51:16Z)
• GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders [7.129137910302658]
  We propose GRAPHCOMP, a graph-based method for error-bounded lossy compression of scientific data.<n>Inspired by Graph Neural Networks (GNNs), we then propose a temporal graph autoencoder to learn latent representations that significantly reduce the size of the graph.<n>Decompression reverses the process and utilizes the learnt graph model together with the latent representation to reconstruct an approximation of the original data.
  arXiv  Detail & Related papers  (2025-05-08T18:58:54Z)
• Fact Checking Beyond Training Set [64.88575826304024]
  We show that the retriever-reader suffers from performance deterioration when it is trained on labeled data from one domain and used in another domain. We propose an adversarial algorithm to make the retriever component robust against distribution shift. We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models.
  arXiv  Detail & Related papers  (2024-03-27T15:15:14Z)
• LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories [59.14011485494713]
  This work introduces a fully automated 2D/3D labeling framework that can generate labels for RGB-D scans at equal (or better) level of accuracy. We demonstrate the effectiveness of our LabelMaker pipeline by generating significantly better labels for the ScanNet datasets and automatically labelling the previously unlabeled ARKitScenes dataset.
  arXiv  Detail & Related papers  (2023-11-20T20:40:24Z)
• Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats [80.12253291709673]
  We propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model.
  arXiv  Detail & Related papers  (2022-12-29T22:22:49Z)
• Supervision by Registration and Triangulation for Landmark Detection [70.13440728689231]
  We present Supervision by Registration and Triangulation (SRT), an unsupervised approach that utilizes unlabeled multi-view video to improve the accuracy and precision of landmark detectors. Being able to utilize unlabeled data enables our detectors to learn from massive amounts of unlabeled data freely available.
  arXiv  Detail & Related papers  (2021-01-25T02:48:21Z)
• Unsupervised Performance Analysis of 3D Face Alignment with a Statistically Robust Confidence Test [32.43769049247355]
  This paper addresses the problem of analysing the performance of 3D face alignment (3DFA) The core ingredient of the proposed methodology is the robust estimation of the rigid transformation between predicted landmarks and model landmarks. The results show that the proposed analysis is consistent with supervised metrics and that it can be used to measure the accuracy of both predicted landmarks and of automatically annotated 3DFA datasets.
  arXiv  Detail & Related papers  (2020-04-14T14:33:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.