Visiting a museum as a kid, I sometimes wondered why I could hardly read anything in the medieval or early modern books and manuscripts displayed in the exhibition. Even after I learned Latin in school, the situation did not improve. I was not aware that people in former centuries used a lot more abbreviations than today, especially in Latin texts. As long as paper (or parchment) was very expensive, scribes and printers tried to save as much space as possible. Therefore, a sentence like “In principio fecit deus caelum et terram” could be abbreviated as “In prīcipio fecit de⁹ celū ⁊ ťrā” (“In the beginning, God created the heavens and earth” — the first verse of the Bible). You may have noted that these abbreviations worked differently than abbreviations like “WWW” or “U.S.” that we are familiar with today, and it would be nice if they could be resolved automatically with a Python script.
In the first episode of this miniseries, I explained how to use OCR with Transkribus to create a diplomatic transcription from images of 16th century Latin prints. However, the resulting text is full of abbreviations and may contain transcription errors. In both cases, Python scripts could help to normalize the text and to detect possible errors.
An indispensable prerequisite for pimping the Transkribus workflow with Python is flawless access to the data stored on the Transkribus server. It would not make much sense to manually download the data, then run some Python scripts on the command line, and finally upload the data again manually, especially when we talk about hundreds of pages to be processed. This episode will show how to download and upload transcriptions from and to the Transkribus server using Python. (Basic to intermediate knowledge of Python is required.)