看见标志：用于广告牌可见性分析的边缘可部署OCR模型综述
Seeing the Signs: A Survey of Edge-Deployable OCR Models for Billboard Visibility Analysis
AI总结：
本研究针对户外广告牌文本可见性验证问题，系统评测了Qwen 2.5 VL 3B等VLMs与PaddleOCRv4在天气干扰数据集上的表现，发现轻量级CNN在边缘部署中仍具优势，并公开了基准与代码。
摘要：
户外广告仍是现代营销的关键媒介，但在真实条件下准确验证广告牌文本可见性仍具挑战。传统光学字符识别（OCR）流程擅长裁剪文本识别，但常难以应对复杂户外场景、多变字体及天气导致的视觉噪声。近期，多模态视觉语言模型（VLMs）作为有前景的替代方案出现，提供无需显式检测步骤的端到端场景理解。本研究系统地对代表性VLMs（包括Qwen 2.5 VL 3B、InternVL3和SmolVLM2）与紧凑型基于CNN的OCR基线（PaddleOCRv4）进行基准测试，测试在两个公开数据集（ICDAR 2015和SVT）上进行，并加入合成天气失真以模拟真实退化。结果显示，尽管部分VLMs擅长整体场景推理，但轻量级CNN流程在极低计算成本下仍对裁剪文本保持有竞争力的准确率——这是边缘部署的重要考量。为促进未来研究，我们公开发布了天气增强基准和评估代码。
英文摘要：
Outdoor advertisements remain a critical medium for modern marketing, yet accurately verifying billboard text visibility under real-world conditions is still challenging. Traditional Optical Character Recognition (OCR) pipelines excel at cropped text recognition but often struggle with complex outdoor scenes, varying fonts, and weather-induced visual noise. Recently, multimodal Vision-Language Models (VLMs) have emerged as promising alternatives, offering end-to-end scene understanding with no explicit detection step. This work systematically benchmarks representative VLMs - including Qwen 2.5 VL 3B, InternVL3, and SmolVLM2 - against a compact CNN-based OCR baseline (PaddleOCRv4) across two public datasets (ICDAR 2015 and SVT), augmented with synthetic weather distortions to simulate realistic degradation. Our results reveal that while selected VLMs excel at holistic scene reasoning, lightweight CNN pipelines still achieve competitive accuracy for cropped text at a fraction of the computational cost-an important consideration for edge deployment. To foster future research, we release our weather-augmented benchmark and evaluation code publicly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
