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
Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their robustness under controlled visual degradation remains insufficiently understood. This gap is critical for OCR reasoning, where visual corruption can induce OCR errors and structural distortions, thereby introducing uncertainty into the reasoning task.
arXiv:2609.00232v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on OCR-centric document understanding and text-rich visual reasoning benchmar...
By Yue Zhou, Yuan Wu, Yi Chang
arXiv:2608.22366v1 Announce Type: new
Abstract: Vision-language models (VLMs) are increasingly being used for document understanding, yet their role in Arabic and Islamic manuscript recognition remai...
By Moshiur Farazi, Firoj Alam, Abderrahmane Maaradji, Zakaria Maamar, Hamdy Mubarak, Wajdi Zaghouani
arXiv:2607. 08143v1 Announce Type: cross Abstract: We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents.
By Maud Ehrmann, Emanuela Boros, Juri Opitz, Andrianos Michail, Florian Wagner, Simon Clematide
We present HunyuanOCR-1. 5, a lightweight end-to-end OCR-specialized vision-language model.