The growing volume of video-based news content has heightened the need for transparent and reliable methods to extract on-screen information. Yet the variability of graphical layouts, typographic conventions, and platform-specific design patterns renders manual indexing impractical. This work presents a comprehensive framework for automatically detecting and extracting personal names from broadcast and social-media-native news videos. It introduces a curated and balanced corpus of annotated frames capturing the diversity of contemporary news graphics and proposes an interpretable, modular extraction pipeline designed to operate under deterministic and auditable conditions. The pipeline is evaluated against a contrasting class of generative multimodal methods, revealing a clear trade-off between deterministic auditability and stochastic inference. The underlying detector achieves 95.8% mAP@0.5, demonstrating operationally robust performance for graphical element localisation. While generative systems achieve marginally higher raw accuracy (F1: 84.18% vs 77.08%), they lack the transparent data lineage required for journalistic and analytical contexts. The proposed pipeline delivers balanced precision (79.9%) and recall (74.4%), avoids hallucination, and provides full traceability across each processing stage. Complementary user findings indicate that 59% of respondents report difficulty reading on-screen names in fast-paced broadcasts, underscoring the practical relevance of the task. The results establish a methodologically rigorous and interpretable baseline for hybrid multimodal information extraction in modern news media.

翻译：随着视频新闻内容的日益增长，对透明可靠方法提取屏幕信息的需求愈发迫切。然而，图形布局、排版惯例和平台特定设计模式的多样性使得人工索引变得不切实际。本研究提出了一种综合性框架，用于自动检测和提取来自广播及社交媒体原生新闻视频中的人名。该框架引入了一个经过精心筛选且平衡的标注帧语料库，涵盖了当代新闻图形的多样性，并提出了一种可解释的模块化提取流程，旨在在确定性和可审计条件下运行。该流程与一类对比性的生成式多模态方法进行了评估比较，揭示了确定性可审计性与随机推理之间的明确权衡。基础检测器实现了95.8%的mAP@0.5，表明其在图形元素定位方面具有稳健的操作性能。虽然生成式系统获得了略高的原始准确率（F1分数：84.18% 对比 77.08%），但它们缺乏新闻和分析场景所需的透明数据溯源。所提出的流程实现了平衡的精确率（79.9%）和召回率（74.4%），避免了幻觉生成，并在每个处理阶段提供了完整的可追溯性。补充性用户研究发现，59%的受访者表示在快节奏的广播中难以阅读屏幕上的姓名，这凸显了该任务的实际相关性。研究结果为现代新闻媒体中的混合多模态信息提取建立了一个方法论严谨且可解释的基准。