In 1550, the Catholic cathedral preacher Johann Wild (Ioannes Ferus, 1495–1554) admitted in the preface to his commentary on the Gospel of John that he reused texts of Protestant authors such as Johannes Brenz (1499–1570) and Johannes Oekolampad (1482–1531) in his book, but that he only borrowed thoughts that were compatible with Catholic teaching.1 Unfortunately, in his text, there are no footnotes or other references in his text to the authors he presumably cited. Since 1950, however, various historians have found verbatim parallels or at least significant similarities between Wild’s commentaries and Protestant authors.2 But these findings were more or less accidental. It is still unclear to what extend Wild actually quoted Protestant authors, which authors he used in particular, and so on. The main reason for that is that a manual search for verbatim parallels is very time-consuming, even more time-consuming than searching for censorship. So, is there a way to hack the search for literal quotes in 16th century books?
Autor: Markus Müller
Uncovering censorship in the 16th century with Transkribus and Python. Episode V: How did the censors actually change the text?
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
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„Uncovering censorship in the 16th century with Transkribus and Python. Episode V: How did the censors actually change the text?“ weiterlesen
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.)
Uncovering censorship in the 16th century with Transkribus and Python. Episode I: OCR with Latin prints
by Markus Müller with the collaboration of Melina Ramirez
Introduction
Since the 1980s, our knowledge on 16th century censorship has been growing constantly thanks to coordinated research projects and new sources. In over ten years of research, for instance, José Maria Bujanda and his team edited all printed catalogues of forbidden books issued by Catholic authorities in the 16th century (eleven volumes until 1999). This made it very easy to check whether a certain author or book was banned or not. With the opening of the Archive of the Congregation of Faith (the former Roman Inquisition) in 1998 a vast number of manuscripts became available that provided an insight into the internals of the Roman Congregation of the Index (founded in 1572) and the decision making processes of the censors. These processes are particularly interesting for books that were not totally banned but expurged, meaning that the censors deleted or modified passages considered “heretical” and then reprinted the book. In many cases, the only way to find out what they actually changed is to compare the original with the expurged edition line by line and word by word. Because Rome was by far not the only Catholic authority that expurged books, the same book was sometimes also expurged by the Spanish Inquisition, the Sorbonne in Paris and other local inquisitors. They all had their own censoring procedures resulting in different expurgations. If we want to uncover the differences between these expurgations, we have to repeat the tedious manual comparison over and over again. These repetitive comparisons are exactly the kind of “boring stuff” that could be automated with Python. „Uncovering censorship in the 16th century with Transkribus and Python. Episode I: OCR with Latin prints“ weiterlesen