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arXiv Detail & Related papers (2026-08-08T04:50:00Z)
OvisOCR2 Technical Report [23.66283449540541]
We build a data engine that combines filtered real-document annotations with synthetic pages.<n>OvisOCR2 achieves a state-of-the-art overall score of 96.58 on OmniDocBench v1.6.<n>OvisOCR2 also achieves the highest Avg3 score of 75.06 on PureDocBench.
arXiv Detail & Related papers (2026-07-15T09:32:33Z)
ClinOCR-Bench: A Comprehensive Clinical Scanned Document Dataset for Optical Character Recognition Model Evaluation [4.105909125198805]
We release a publicly available, realistic-looking OCR benchmark dataset, ClinOCR-Bench.<n>ClinOCR-Bench features diverse document types and layouts, full coverage of common EHR scan artifacts, protected health information-free.<n> Baseline OCR performance was evaluated using state-of-the-art open-weight and proprietary VLMs.
arXiv Detail & Related papers (2026-07-04T00:19:31Z)
Multimodal OCR: Parse Anything from Documents [72.69545534962234]
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arXiv Detail & Related papers (2026-03-13T14:42:21Z)
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arXiv Detail & Related papers (2026-03-11T16:08:22Z)
AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation [35.07704681580893]
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arXiv Detail & Related papers (2026-02-24T18:57:33Z)
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arXiv Detail & Related papers (2026-01-20T21:26:15Z)
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arXiv Detail & Related papers (2026-01-20T18:58:32Z)
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arXiv Detail & Related papers (2026-01-07T09:01:23Z)
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arXiv Detail & Related papers (2025-11-13T15:12:17Z)
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arXiv Detail & Related papers (2025-11-01T04:54:58Z)
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arXiv Detail & Related papers (2024-05-01T00:30:13Z)
LOCR: Location-Guided Transformer for Optical Character Recognition [55.195165959662795]
We propose LOCR, a model that integrates location guiding into the transformer architecture during autoregression. We train the model on a dataset comprising over 77M text-location pairs from 125K academic document pages, including bounding boxes for words, tables and mathematical symbols. It outperforms all existing methods in our test set constructed from arXiv, as measured by edit distance, BLEU, METEOR and F-measure.
arXiv Detail & Related papers (2024-03-04T15:34:12Z)
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding [55.4806974284156]
Document understanding refers to automatically extract, analyze and comprehend information from digital documents, such as a web page. Existing Multi-model Large Language Models (MLLMs) have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition.
arXiv Detail & Related papers (2023-07-04T11:28:07Z)
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This work releases a dataset of 3000 fully-annotated, real-world newspaper images from 21 different U.S. states. It proposes layout segmentation as a precursor to existing optical character recognition (OCR) engines. It provides a thorough and structured evaluation protocol for isolated layout segmentation and end-to-end OCR.
arXiv Detail & Related papers (2022-02-03T05:21:31Z)
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Learning Contextualized Document Representations for Healthcare Answer Retrieval [68.02029435111193]
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arXiv Detail & Related papers (2020-02-03T15:47:19Z)

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