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English(EN) ERN-Net : Evolving Reason Node-Net for Document Binarization

新的ERN-Net通过演进式推理节点改进文档二值化

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了ERN-Net，一种用于文档二值化的新方法，能够更好地处理退化的图像区域。该方法利用演进式推理节点和多尺度推理来增强模糊笔画、断裂字符和嘈杂背景。实验表明，ConvNeXt-Tiny在准确性和内存效率之间取得了良好的平衡，并且在DIBCO数据集上进行预训练可以在极短的额外训练时间内提升性能。 AI

影响 增强了文档图像处理能力，特别是在低数据和低内存场景下。

排序理由 这是一篇描述用于文档二值化新模型的学术论文。

在 arXiv cs.CV 阅读 →

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

新的ERN-Net通过演进式推理节点改进文档二值化

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Hsin-Jui Pan, Sheng-Wei Chan, Jen-Shiung Chiang ·

    ERN-Net：为文档二值化而演进的推理节点网络

    arXiv:2606.11710v1 Announce Type: new Abstract: This paper presents ERN-Net, an Evolving Reason Node-Net for efficient document image binarization. ERN-Net enhances degradation-sensitive regions, such as faint strokes, broken characters, and noisy backgrounds, through evolving re…

  2. arXiv cs.CV TIER_1 English(EN) · Jen-Shiung Chiang ·

    ERN-Net：为文档二值化而演进的推理节点网络

    This paper presents ERN-Net, an Evolving Reason Node-Net for efficient document image binarization. ERN-Net enhances degradation-sensitive regions, such as faint strokes, broken characters, and noisy backgrounds, through evolving reason nodes and multi-scale reasoning. We further…

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