  Billions of public domain documents remain trapped in hard copy or lack an
accurate digitization. Modern natural language processing methods cannot be
used to index, retrieve, and summarize their texts; conduct computational
textual analyses; or extract information for statistical analyses, and these
texts cannot be incorporated into language model training. Given the diversity
and sheer quantity of public domain texts, liberating them at scale requires
optical character recognition (OCR) that is accurate, extremely cheap to
deploy, and sample-efficient to customize to novel collections, languages, and
character sets. Existing OCR engines, largely designed for small-scale
commercial applications in high resource languages, often fall short of these
requirements. EffOCR (EfficientOCR), a novel open-source OCR package, meets
both the computational and sample efficiency requirements for liberating texts
at scale by abandoning the sequence-to-sequence architecture typically used for
OCR, which takes representations from a learned vision model as inputs to a
learned language model. Instead, EffOCR models OCR as a character or word-level
image retrieval problem. EffOCR is cheap and sample efficient to train, as the
model only needs to learn characters' visual appearance and not how they are
used in sequence to form language. Models in the EffOCR model zoo can be
deployed off-the-shelf with only a few lines of code. Importantly, EffOCR also
allows for easy, sample efficient customization with a simple model training
interface and minimal labeling requirements due to its sample efficiency. We
illustrate the utility of EffOCR by cheaply and accurately digitizing 20
million historical U.S. newspaper scans, evaluating zero-shot performance on
randomly selected documents from the U.S. National Archives, and accurately
digitizing Japanese documents for which all other OCR solutions failed.
