A Hybrid Deterministic Framework for Named Entity Extraction in Broadcast News Video

TL;DR

This paper introduces a deterministic, modular framework for extracting personal names from broadcast news videos, emphasizing transparency, robustness, and interpretability over purely stochastic methods.

Contribution

It presents a novel, interpretable extraction pipeline with a curated dataset, balancing accuracy and transparency, and compares it against generative multimodal approaches.

Findings

Detector achieves 95.8% mAP@0.5 for graphical element localization.

Generative methods have slightly higher F1 scores but lack transparency.

Proposed method offers balanced precision and recall with full traceability.

Abstract

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.