From the post:
Optical character recognition (OCR) is the process of extracting written or typed text from images such as photos and scanned documents into machine-encoded text. The new rOpenSci package tesseract brings one of the best open-source OCR engines to R. This enables researchers or journalists, for example, to search and analyze vast numbers of documents that are only available in printed form.
People looking to extract text and metadata from pdf files in R should try our pdftools package.
Reading too quickly at first I thought I had missed a new version of Tesseract (tesseract-ocr Github), an OCR program that I use on a semi-regular basis.
Reading a little slower, ;-), I discovered Ooms is describing a new package for R, which uses Tesseract for OCR.
This is great news but be aware that Tesseract (whether called by an R package or standalone) can generate a large amount of output in a fairly short period of time.
One of the stumbling blocks of OCR is the labor intensive process of cleaning up the inevitable mistakes.
Depending on how critical accuracy is for searching, for example, you may choose to verify and clean only quotes for use in other publications.
Best to make those decisions up front and not be faced with a mountain of output that isn’t useful unless and until it has been corrected.