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English(EN) 2D Rotary Position Embedding for Scene Text Recognition with Transformers

新的二维位置编码提升Transformer场景文本识别能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的二维旋转位置嵌入（2D-RoPE-STR）方法，以改进基于Transformer的场景文本识别（STR）。这种新方法通过更好地捕捉文本图像的二维空间结构来解决现有的一维位置编码的局限性，这对于处理弯曲、旋转和透视失真的文本至关重要。该方法引入了各向异性维度分配，并将旋转耦合扩展到编码器-解码器交叉注意力中，从而实现更准确的自回归解码。在六个标准基准上的评估显示，性能有了显著提升，尤其是在处理不规则文本布局方面。 AI

影响 增强了Transformer模型在复杂、真实图像条件下识别文本的能力。

排序理由 该集群包含一篇详细介绍场景文本识别新方法的学术论文。

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 →

新的二维位置编码提升Transformer场景文本识别能力

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该集群包含一篇详细介绍场景文本识别新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Zobeir Raisi ·

    用于 Transformer 场景文本识别的二维旋转位置嵌入

    arXiv:2607.13458v1 Announce Type: new Abstract: Scene Text Recognition (STR) remains challenging due to the diversity of text appearances, including curvature, rotation, and perspective distortion. Recent Transformer-based approaches perform well but usually rely on one-dimension…

  2. arXiv cs.CV TIER_1 English(EN) · Zobeir Raisi ·

    用于 Transformer 场景文本识别的二维旋转位置嵌入

    Scene Text Recognition (STR) remains challenging due to the diversity of text appearances, including curvature, rotation, and perspective distortion. Recent Transformer-based approaches perform well but usually rely on one-dimensional positional encodings that ignore the 2D spati…

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