We have come a long way since episode I of this miniseries: After digitizing the texts, normalizing the orthographic variants, and resolving the abbreviations, we used an interactive web app to find and correct remaining transcription errors. Now that the texts are free of mistakes we can finally use them for comparisons. In this episode, we will compare an original text with an expurged reprint to find censorship. Since the censors sometimes manipulated only one or two characters in a word, thereby changing the meaning of the whole sentence, we will compare the texts word by word using the Python module difflib.
„Uncovering censorship in the 16th century with Transkribus and Python. Episode V: How did the censors actually change the text?“ weiterlesen
Schlagwort: programming
Uncovering censorship in the 16th century with Transkribus and Python. Episode IV: Detecting OCR transcription errors
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!
Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text
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.
Uncovering censorship in the 16th century with Transkribus and Python. Episode II: Let Python speak to Transkribus
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.)