I'm trying to look at a series of unusual words taken from OCR and determine which ones merit further investigation to see where/if the OCR screwed up. I've tried a few different approaches like comparing the words to existing dictionaries and so on, but I still have a large number of words to look at manually.
What I would like to do: Send my list of words to Google's ngrams, starting from a particular year (say 1950), and get the average ngram frequency to reduce my list to the real outliers.
What I have tried:
I thought I could use the ngramr package for this, but it can only send 12 words at a time, and I have a list of thousands of words. I was hoping I could save myself some time doing it this way. Perhaps there is some way of dividing my dataset into separate groups of no larger than 12 and then send them all to this package, but it doesn't seem like it is intended for this kind of approach.
Is there a better/smarter way of doing this? Ideally I would like to avoid having to export the data into Python because I want my code to be fully reproducible by someone who is only familiar with R.
Any help would be hugely appreciated.