Hawaiian (ʻŌlelo Hawaiʻi) is an endangered Polynesian language whose broadcast archives represent a critical yet underutilized resource for language documentation. We present the first evaluation of vision-language models (VLMs) for structured entity extraction from television chyrons, investigating the performance gap between Hawaiian-language content and mainland U.S. comparisons. Using our new HiChy dataset of 3,925 manually annotated images, we demonstrate that Hawaiian content remains significantly more challenging for current VLMs: for the best-performing model (Qwen2.5-VL-7B), character error rates roughly double from 0.064 on mainland data to 0.130 on Hawaiian content. We extend the task to key information extraction (KIE), finding that while models can perform structured parsing, they struggle specifically with names of Hawaiian linguistic origin, a difficulty that persists even when controlling for geographic source. Across five evaluated models spanning local quantized inference and commercial APIs, we find that OCR accuracy and structured extraction capability do not necessarily correlate: the best OCR model (Gemini 3 Flash) underperforms locally-deployed alternatives on KIE, while even a 2.2B-parameter model (SmolVLM2) achieves functional extraction. Our results provide a baseline for AI-assisted archival processing of underrepresented language media and highlight the need for models that better account for the orthographic and cultural specificities of Hawaiian.

Publicly available spoken language identification (LID) systems provide sparse and inconsistent coverage of indigenous languages of the Americas and languages of the Pacific Islands. No system on HuggingFace covers Central Alaskan Yup’ik except the largest variant of Meta’s MMS-LID family, and only three MMS-LID variants cover Samoan, while Whisper and VoxLingua107-based models lack both despite including other Polynesian languages. We describe an ongoing effort to build a coarse-labeled LID dataset for Yup’ik and Samoan from US public broadcast archives, benchmark publicly available LID systems on it, and train a simple MLP classifier on frozen wav2vec~2.0 representations as a prototype. We report preliminary corpus statistics, off-the-shelf model performance, and prototype results. Guided by the distinctive phonological typology of the target languages, we outline a phonologically-informed fine-tuning direction as future work.