  There has been recent interest in improving optical character recognition
(OCR) for endangered languages, particularly because a large number of
documents and books in these languages are not in machine-readable formats. The
performance of OCR systems is typically evaluated using automatic metrics such
as character and word error rates. While error rates are useful for the
comparison of different models and systems, they do not measure whether and how
the transcriptions produced from OCR tools are useful to downstream users. In
this paper, we present a human-centric evaluation of OCR systems, focusing on
the Kwak'wala language as a case study. With a user study, we show that
utilizing OCR reduces the time spent in the manual transcription of culturally
valuable documents -- a task that is often undertaken by endangered language
community members and researchers -- by over 50%. Our results demonstrate the
potential benefits that OCR tools can have on downstream language documentation
and revitalization efforts.
