  There is little to no data available to build natural language processing
models for most endangered languages. However, textual data in these languages
often exists in formats that are not machine-readable, such as paper books and
scanned images. In this work, we address the task of extracting text from these
resources. We create a benchmark dataset of transcriptions for scanned books in
three critically endangered languages and present a systematic analysis of how
general-purpose OCR tools are not robust to the data-scarce setting of
endangered languages. We develop an OCR post-correction method tailored to ease
training in this data-scarce setting, reducing the recognition error rate by
34% on average across the three languages.
