Uncovering censorship in the 16th century with Transkribus and Python. Episode IV: Detecting OCR transcription errors

by Markus Müller

In the last episode, we built a pipeline to convert a diplomatic transcription into normalized Latin text. The code works fine as long as the diplomatic transcription is correct. But what happens if the transcription contains errors or, even worse, if the printer in the 16th century misspelled a word? – Right now, nothing would happen at the moment because our pipeline cannot detect these errors. This is a problem because as soon as we start comparing two editions of the same text to check for censorship (and that’s where we are going!), the slightest difference between the two texts may be interpreted as censorship. Can we solve this problem? – Yes, we can!

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