OCR-EDR is a rendering‑aware framework that diagnoses and repairs OCR errors by comparing an editable OCR prediction with its rendered image. It jointly assesses consistency, localizes genuine errors, and applies executable edits, optionally requesting updated renderings for iterative reassessment. On the newly constructed OCRErrBench, the DocEDR model achieves 94.78% diagnostic accuracy and repairs 86.23% of erroneous inputs, improving formula metrics by up to 30.99 percentage points on benchmark datasets.
By Linnan Zhao, Kang Liu, Hao Yu, Jiabo Zhan, Chong Sun, Chen Li
OCR-EDR is a rendering‑aware framework that diagnoses and repairs OCR errors by jointly evaluating an OCR prediction, its editable form, and the rendered image of the source document. It localizes genuine mistakes while preserving valid or rendering‑equivalent predictions, then applies executable edits and iteratively reassesses with updated renderings. On the newly constructed OCRErrBench, the DocEDR model achieves 94.78% diagnostic accuracy and repairs 86.23% of errors, boosting formula metrics by up to 30.99 percentage points and improving CDM scores on several OCR systems.
arXiv:2609.14521v1 Announce Type: new
Abstract: Recovering editable 3D parametric curves from 2D images is a fundamental challenge in computer graphics, bridging pixel-based perception and vector-bas...
By Zhirui Gao, Renjiao Yi, Yunfan Ye, Ruizhen Hu, Chenyang Zhu, Wei Chen, Kai Xu
arXiv:2606. 31764v2 Announce Type: replace-cross Abstract: Differentiable rendering of planar rational splines remains largely underexplored, despite their widespread use in vector graphics and design.
By Jingye Qiu, Shizhe Zhou
PlotPick is an open‑source tool that employs vision‑language models (VLMs) to batch‑extract tabular data from scientific figures. In benchmarks on synthetic chart datasets (ChartX and PlotQA), nine VLMs generally outperform the dedicated chart‑to‑table model DePlot, especially on box plots and horizontal bar charts. The tool’s figure detection, output formatting, and default model (Claude Haiku 4.5) were not evaluated, and accuracy on real biomedical figures remains unverified.
By Tommy Carstensen
The paper introduces Sharp Mode Connectivity (SMC), a new formulation of mode connectivity that optimizes for flatness in the entire local neighborhood of a weight-space path rather than just along the path itself. By applying a sharpness-aware approximation to a minimax objective, SMC produces low-loss curves that remain robust to distribution shifts, achieving significant accuracy gains on CIFAR‑10‑C and outperforming standard mode connectivity on several architectures.
By Alejandro Calatrava, Xu Zhang, Ren Wang