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New CC-OCR V2 benchmark reveals LMMs fall short in real-world document processing

作者 PulseAugur 编辑部 · [2 个来源] ·

A new benchmark, CC-OCR V2, has been released to evaluate Large Multimodal Models (LMMs) on real-world document processing tasks. The benchmark includes 7,093 challenging samples across five OCR-centric tracks, addressing limitations of existing benchmarks that do not reflect practical application conditions. Experiments with 14 advanced LMMs showed significant performance degradation, highlighting a gap between current model capabilities and real-world requirements. AI

影响 Highlights a gap in LMM performance for real-world document processing, suggesting current models may not meet enterprise needs.

排序理由 The cluster describes a new academic paper introducing a benchmark dataset for evaluating AI models.

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

New CC-OCR V2 benchmark reveals LMMs fall short in real-world document processing

报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Zhipeng Xu, Junhao Ji, Zulong Chen, Zhenghao Liu, Qing Liu, Chunyi Peng, Zubao Qin, Ze Xu, Jianqiang Wan, Jun Tang, Zhibo Yang, Shuai Bai, Dayiheng Liu ·

    CC-OCR V2: Benchmarking Large Multimodal Models for Literacy in Real-world Document Processing

    arXiv:2605.03903v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have recently shown strong performance on Optical Character Recognition (OCR) tasks, demonstrating their promising capability in document literacy. However, their effectiveness in real-world applicatio…

  2. arXiv cs.CL TIER_1 English(EN) · Dayiheng Liu ·

    CC-OCR V2: Benchmarking Large Multimodal Models for Literacy in Real-world Document Processing

    Large Multimodal Models (LMMs) have recently shown strong performance on Optical Character Recognition (OCR) tasks, demonstrating their promising capability in document literacy. However, their effectiveness in real-world applications remains underexplored, as existing benchmarks…

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