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} </style> <link rel="dc:identifier" href="https://dhlab.hypotheses.org/2154"/> <meta property="dc:title" content="Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text"/> <meta property="dc:creator" content="Markus Müller"/> <meta property="dcterms:created" content="2021-09-03"/> <meta property="dcterms:abstract" content="by Markus Müller 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 … „Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text“ weiterlesen" xml:lang="fr"/> <meta property="dc:subject" content="Lab Report"/> <meta property="dc:subject" content="digital humanities"/> <meta property="dc:subject" content="OCR"/> <meta property="dc:subject" content="programming"/> <meta property="dc:subject" content="Python"/> <meta property="dc:subject" content="Regular Expressions"/> <meta property="dc:subject" content="Transkribus"/> <meta property="dc:type" content="Billet"/> <link rel="alternate" type="application/opml+xml" title="Outline" href="https://web.archive.org/web/20211030225402/https://www.openedition.org/opml.php?pubtype=carnet"/></head> <body data-cmplz="1" class="post-template-default single single-post postid-2154 single-format-standard custom-background wp-embed-responsive wp-featherlight-captions group-blog"> <div id="page" class="site"> <div class="site-inner"> <a class="skip-link screen-reader-text" href="#content">Zum Inhalt springen</a> <header id="masthead" class="site-header" role="banner"> <div class="site-header-main"> <div class="site-branding"> <p class="site-title"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/" rel="home">Digital Humanities Lab</a></p> <p class="site-description">Leibniz-Institut für Europäische Geschichte</p> </div> <button id="menu-toggle" class="menu-toggle">Menü</button> <div id="site-header-menu" class="site-header-menu"> <nav id="site-navigation" class="main-navigation" role="navigation" aria-label="Primäres Menü"> <div class="menu-menue-container"><ul id="menu-menue" class="primary-menu"><li id="menu-item-145" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-home menu-item-145"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/das-dh-lab-am-ieg">About</a></li> <li id="menu-item-1140" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-1140"><a href="https://web.archive.org/web/20211030225402/https://github.com/ieg-dhr">DH Lab @ GitHub</a></li> <li id="menu-item-1590" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-1590"><a href="https://web.archive.org/web/20211030225402/https://www.zotero.org/groups/2449547/ieg_dh_lab/library">DH Lab @ Zotero</a></li> <li id="menu-item-1866" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-1866"><a href="https://web.archive.org/web/20211030225402/https://soundcloud.com/transitsofmars/dh-lab">DH Lab – The Anthem</a></li> </ul></div> </nav> </div> </div> </header> <div id="content" class="site-content"> <div id="primary" class="content-area"> <main id="main" class="site-main" role="main"> <article id="post-2154" class="post-2154 post type-post status-publish format-standard has-post-thumbnail hentry category-lab-report tag-digital-humanities tag-ocr tag-programming tag-python tag-regular-expressions tag-transkribus"> <header class="entry-header"> <h1 class="entry-title">Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text</h1> </header> <div class="post-thumbnail"> <img width="825" height="510" src="https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/06/transkribus_0_Titel-825x510.jpg" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" loading="lazy" srcset="https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/06/transkribus_0_Titel-825x510.jpg 825w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/06/transkribus_0_Titel-300x185.jpg 300w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/06/transkribus_0_Titel-500x309.jpg 500w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/06/transkribus_0_Titel-768x475.jpg 768w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/06/transkribus_0_Titel.jpg 1000w" sizes="(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 60vw, (max-width: 1362px) 62vw, 840px"/> </div> <div class="entry-content"> <p><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/author/muellerm">by Markus Müller</a></p> <p>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 pr<span style="color: #ff0000"><strong>ī</strong></span>cipio fecit de<span style="color: #ff0000"><strong>⁹</strong></span> cel<span style="color: #ff0000"><strong>ū</strong> <strong>⁊</strong> <strong>ť</strong></span>r<span style="color: #ff0000"><strong>ā</strong></span>” (“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.</p> <p><span id="more-2154"></span></p> <hr/> <p><a name="miniseries"></a></p> <p><span style="color: #808080;font-size: 11pt">This miniseries describes a digital workflow that starts with raw images of 16th century Latin prints, extracts the text, normalizes it, corrects transcription errors, and finally compares two texts to uncover differences, i.e. censorship. These are the episodes:</span></p> <ol> <li><span style="color: #808080;font-size: 11pt"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2022#start">OCR with 16th century prints.</a></span></li> <li><span style="color: #808080;font-size: 11pt"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2114">Let Python speak to Transkribus.</a></span></li> <li><span style="color: #808080;font-size: 11pt">Normalizing 16th century raw text.</span></li> <li><span style="color: #808080;font-size: 11pt"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2191">Detecting OCR transcription errors.</a><br/> </span></li> <li><span style="color: #808080;font-size: 11pt"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/?p=2271">How did the censors actually change the text?</a><br/> </span></li> <li><span style="color: #808080;font-size: 11pt">Finding text re-use. (coming soon)</span></li> </ol> <hr/> <figure id="attachment_2156" aria-describedby="caption-attachment-2156" style="width: 679px" class="wp-caption alignnone"><a href="https://web.archive.org/web/20211030225402/http://dhlab.hypotheses.org/files/2021/09/transkribus_3_in_principio.jpg"><img loading="lazy" class="wp-image-2156 size-full" src="https://web.archive.org/web/20211030225402im_/http://dhlab.hypotheses.org/files/2021/09/transkribus_3_in_principio.jpg" alt="" width="679" height="111" srcset="https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_in_principio.jpg 679w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_in_principio-300x49.jpg 300w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_in_principio-500x82.jpg 500w" sizes="(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 61vw, (max-width: 1362px) 45vw, 600px"/></a><figcaption id="caption-attachment-2156" class="wp-caption-text">Abbreviations in Nicolaus de Lyra’s commentary on the <a href="https://web.archive.org/web/20211030225402/https://daten.digitale-sammlungen.de/bsb00001917/images/index.html?id=00001917&seite=50&fip=193.174.98.30&nativeno=&groesser=300%25">first verse</a> of the book of Genesis, printed in Basel 1501 (<a href="https://web.archive.org/web/20211030225402/http://gateway-bayern.de/VD16+B+2581">VD16 B 2581</a>).</figcaption></figure> <p>Just like today, words could be represented by their initial letters (like “a.d.” for “anno domini”, “in the year of the Lord”). Some abbreviations of this type are still in use, such as “etc.” for “et caetera” (“and so on”). In those days, however, “etc.” could also be written as “et caet.”, “& caet.”, “&c.”, or even “⁊c.”. The last example, by the way, starts with a so-called “tironian et” (“⁊”) which was already used in ancient times as part of a complex system of shorthand invented by a Roman scribe called Tiro, the so-called “<a href="https://web.archive.org/web/20211030225402/https://en.wikipedia.org/wiki/Tironian_notes">Tironian notes</a>”. While the “⁊” almost completely disappeared, the ampersand “&” made it to our keyboards. In the example from above, the same idea of replacing a syllable by a mark with an independent meaning is used in “de⁹” where “⁹” stands for “us”, so “de⁹” means “deus” (“God”). And since the syllable “ter” could be replaced by “ť”, “terra” (“earth”) could be abbreviated as “ťra”.</p> <p>In some cases, our medieval and early modern friends even had to know some Greek to get the point: “Jesus” was abbreviated as “IHS” or “ihs”/“jhs”, using the first three letters of the name written in Greek: “ΙΗΣΟΥΣ” (the Greek sigma “Σ” was transliterated into a Latin “S”). The same could be done with “Christ” = “Christos” = “ΧΡΙΣΤΟΣ” = “XP” or “xp”. If you visit an old church, you may find these abbreviations on reliefs, sculptures or paintings, sometimes contracted to “<a href="https://web.archive.org/web/20211030225402/https://en.wiktionary.org/wiki/%E2%98%A7#Translingual">☧</a>”; or take a look at the <a href="https://web.archive.org/web/20211030225402/https://upload.wikimedia.org/wikipedia/commons/6/67/Insigne_Francisci.svg">coat of arms of pope Francis I.</a></p> <p>As you can see, scribes (and printers) were quite creative at that time. Things get even worse when an abbreviation can be resolved in various ways, as it is the case with the straight bar above a vowel, called “<a href="https://web.archive.org/web/20211030225402/https://en.wikipedia.org/wiki/Macron_(diacritic)">macron</a>”. There are three macrons in the example from above: “prīcipio” = “principio” (“in the beginning”), “celū” = “celum”, or in classical spelling “caelum” (“heaven”), and “ťrā” = “terram” (“earth”). In the first case, the macron replaces an “n”; in the other two, an “m”. You need to know your vocabulary and grammar to make the right choice between “n” and “m”.</p> <p>These examples are only a very small part of a large and complex system of abbreviations which was in use back then. To read heavily abbreviated texts fluently, you need a certain palaeographic expertise and a lot of training. Apart from a growing number of tutorials<sup><a id="fnr.1" class="footref" href="#fn.1">1</a></sup>, the best resource to get started is the “Lexicon abbreviaturarum”, a dictionary of thousands of abbreviations compiled by an Italian archivist in Parma, <a href="https://web.archive.org/web/20211030225402/https://de.wikipedia.org/wiki/Adriano_Cappelli">Adriano Cappelli</a> (1859–1942, no English Wikipedia article yet!). The first three editions in Italian are available online (e.g. the <a href="https://web.archive.org/web/20211030225402/https://archive.org/details/CappelliDizionarioDiAbbreviature">third edition</a>) as is a <a href="https://web.archive.org/web/20211030225402/https://archive.org/details/LexiconAbbreviaturarum">German translation</a>. Cappelli’s introduction will help you to understand different types of abbreviations and to solve most of the palaeographic problems with medieval and early modern Latin texts. Thanks to a crowd-sourcing initiative in 2015, the dictionary has been converted into a searchable <a href="https://web.archive.org/web/20211030225402/https://www.adfontes.uzh.ch/en/ressourcen/abkuerzungen/cappelli-online">database</a>.</p> <h2>Resolving abbreviations with Python and Regular Expressions</h2> <p>The abbreviations themselves were more or less standardized, but their use was not. Thus, one and the same sentence could be written very differently. Compare the following passages taken from two different editions of the same text:</p> <figure id="attachment_2158" aria-describedby="caption-attachment-2158" style="width: 1075px" class="wp-caption alignnone"><a href="https://web.archive.org/web/20211030225402/http://dhlab.hypotheses.org/files/2021/09/transkribus_3_transcription_comparison_Wild1550_Wild1577_highlighted.jpg"><img loading="lazy" class="wp-image-2158 size-full" src="https://web.archive.org/web/20211030225402im_/http://dhlab.hypotheses.org/files/2021/09/transkribus_3_transcription_comparison_Wild1550_Wild1577_highlighted.jpg" alt="" width="1075" height="336" srcset="https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_transcription_comparison_Wild1550_Wild1577_highlighted.jpg 1075w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_transcription_comparison_Wild1550_Wild1577_highlighted-300x94.jpg 300w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_transcription_comparison_Wild1550_Wild1577_highlighted-500x156.jpg 500w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_transcription_comparison_Wild1550_Wild1577_highlighted-768x240.jpg 768w" sizes="(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 1362px) 62vw, 840px"/></a><figcaption id="caption-attachment-2158" class="wp-caption-text">A passage taken from the commentary on the Gospel of John by John Wild (Iohannes Ferus) printed in Mainz (Franz Behem) 1550 and in Rome (in aedibus Populi Romani) 1577.</figcaption></figure> <p>A human reader who knows how to resolve abbreviations would consider the two snippets to be identical. A computer, however, would compare the text letter by letter and find many differences, not only on the level of abbreviations (“<span style="color: #ff0000">Prędicamus</span>/<span style="color: #ff0000">Prædicamus</span>”, “<span style="color: #0000ff">Christū</span>/<span style="color: #0000ff">Christum</span>”, “<span style="color: #339966">&c̄.</span>/<span style="color: #339966">&c.</span>”), but also regarding the usage of interchangeable characters (“<span style="color: #ff00ff">uerum</span>/<span style="color: #ff00ff">verum</span>”, “egreſsi/egreſſi” — “ſ” is just another form of writing “s”). And there is another problem: hyphenation. If there is a hyphen at the end of a line (like in “ſtulti- | tiam”), it would be easy for a computer to join the two parts. But often the hyphen is missing (as in “<span style="color: #ff0000">Præ</span> | <span style="color: #ff0000">dicamus</span>”), and a computer could easily misinterpret the two parts as two different words.</p> <p>If we really want to find censorship automatically (as promised in <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2022">episode I</a>), we have to find a way to overcome all these problems. One approach would be to normalize both texts in a way that their normalized versions are identical. For example, “Prędicamus Christū” and “Prædicamus Christum” should both be transformed into “Praedicamus Christum”. That means, the computer should search for problematic characters like “ę”, “æ”, or “ū” and normalize them.</p> <p>However, a simple search and replace operation will fail, because there are cases where problematic characters should be replaced only if they occur in a certain position within a word or in a certain context. Example: Sometimes the word “cum” (“with”) is written as “quum”. If “quum” were always replaced with “cum”, other words such as “antiquum” (“old”) would also be changed to “anticum”, which is obviously wrong. To prevent the script from doing this, we need a way to say, “Please, replace ‘quum’ with ‘cum’ only when ‘quum’ is a word by itself.” So it’s all about pattern matching. We should be able to define a search pattern like “quum as a word by itself”, or, more formally, <code>[word boundary]</code> <code>quum</code> <code>[word boundary]</code>.</p> <hr/> <h4>REGULAR EXPRESSIONS</h4> <p><span style="color: #808080;font-size: 11pt"><a href="https://web.archive.org/web/20211030225402/https://en.wikipedia.org/wiki/Regular_expression#History">In the 1960s</a>, a formal language called “regular expressions” (“regex”) has been developed to solve this kind of pattern matching problems. A regular expression is a sequence of characters that describes a search pattern. These patterns consist of normal characters (like “A”, “x” or “3”) and “metacharacters” which have a special meaning. For example, <code>.</code> in a regular expression matches “any character”. If you are looking for an actual full stop, you have to search for <code>\.</code>. The metacharacter <code>\s</code> matches “any whitespace character”, <code>\w</code> means “any word character”, and <code>\d</code> stands for “a digit”. Searching for <code>\d\d\.\d\d\.\d\d\d\d</code> would match things like “14.11.1483” or “15.06.1520”, but not “14 November 1483” or “1483-11-14”. To learn more about all the metacharacters available and the logic behind regular expressions, check out a good <a href="https://web.archive.org/web/20211030225402/https://github.com/ziishaned/learn-regex">tutorial</a> and play around with the <a href="https://web.archive.org/web/20211030225402/https://regex101.com/">regex101</a> web app, which provides a live preview of the matches along with extensive help.</span></p> <hr/> <p>Let’s use regex to solve the quum/cum problem: The metacharacter <code>\b</code> matches a “word boundary”, so <code>\bquum\b</code> matches “quum as a word by itself”. This is exactly what we need. Give this solution a test <a href="https://web.archive.org/web/20211030225402/https://regex101.com/r/T4BKbe/2">on regex101</a>. In Python, the built-in <code>re</code> module enables us to use regular expressions in our code. Note that the <code>\bquum\b</code> pattern must be a raw string, i.e. we have to put an “r” before the first quote (<code>r"\bquum\b"</code>), otherwise the <code>\b</code> metacharacter will not work. (Read the <a href="https://web.archive.org/web/20211030225402/https://docs.python.org/3/library/re.html">docs of the <code>re</code> module</a> to understand why!)</p> <pre style="color: #d1d1d1;background: #000000"><span style="color: #e66170;font-weight: bold">import</span> re <span style="color: #9999a9"># Provide the lines as a list:</span> lines <span style="color: #d2cd86">=</span> <span style="color: #d2cd86">[</span><span style="color: #00c4c4">"Prędicamus Christū"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"Prædicamus Christum"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"antiquum quum equum aequum"</span><span style="color: #d2cd86">]</span> <span style="color: #9999a9"># Provide the regex patterns and the corresponding replacements</span> <span style="color: #9999a9"># as a list of lists:</span> replacement_table <span style="color: #d2cd86">=</span> <span style="color: #d2cd86">[</span><span style="color: #d2cd86">[</span><span style="color: #00c4c4">"æ"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"ae"</span><span style="color: #d2cd86">]</span><span style="color: #d2cd86">,</span> <span style="color: #d2cd86">[</span><span style="color: #00c4c4">"ę"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"ae"</span><span style="color: #d2cd86">]</span><span style="color: #d2cd86">,</span> <span style="color: #d2cd86">[</span><span style="color: #00c4c4">"ū"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"um"</span><span style="color: #d2cd86">]</span><span style="color: #d2cd86">,</span> <span style="color: #9999a9"># Make sure to use a raw string (r"PATTERN") here:</span> <span style="color: #d2cd86">[</span><span style="color: #00c4c4">r"\bquum\b"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"cum"</span><span style="color: #d2cd86">]</span><span style="color: #d2cd86">]</span> <span style="color: #9999a9"># Loop through the lines:</span> <span style="color: #e66170;font-weight: bold">for</span> line <span style="color: #e66170;font-weight: bold">in</span> lines<span style="color: #d2cd86">:</span> <span style="color: #e66170;font-weight: bold">print</span><span style="color: #d2cd86">(</span><span style="color: #00c4c4">"raw: "</span><span style="color: #d2cd86">,</span> line<span style="color: #d2cd86">)</span> <span style="color: #9999a9"># Loop through the items in the replacement_table</span> <span style="color: #9999a9"># to apply all the predefined search and replace operations:</span> <span style="color: #e66170;font-weight: bold">for</span> pattern<span style="color: #d2cd86">,</span> replacement <span style="color: #e66170;font-weight: bold">in</span> replacement_table<span style="color: #d2cd86">:</span> line <span style="color: #d2cd86">=</span> re<span style="color: #d2cd86">.</span>sub<span style="color: #d2cd86">(</span>pattern<span style="color: #d2cd86">,</span> replacement<span style="color: #d2cd86">,</span> line<span style="color: #d2cd86">,</span> flags<span style="color: #d2cd86">=</span>re<span style="color: #d2cd86">.</span>IGNORECASE<span style="color: #d2cd86">)</span> <span style="color: #e66170;font-weight: bold">print</span><span style="color: #d2cd86">(</span><span style="color: #00c4c4">"cooked:"</span><span style="color: #d2cd86">,</span> line<span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"</span><span style="color: #008080">\n</span><span style="color: #00c4c4">"</span><span style="color: #d2cd86">)</span> </pre> <p>Listing 1: <code>simple_regex.py</code> on <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/master/simple_regex.py">Github</a>.</p> <p>The <code>simple_regex.py</code> script works fine, but it would be better to outsource the replacement table to an actual table document. It is much easier to modify the table using LibreOffice or the like than changing the Python code directly. We should also try to handle the raw string problem more elegantly and improve some other details. Last but not least, the script should be wrapped as a function (<a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/3dd6caf20bef0dfe2db0e3d3f2882ba9dfc5098a/cleaner.py#L21-L45"><code>.resolve_abbreviations()</code></a>) as part of a class — let’s call it <code>Cleaner</code> — to make it more (re-)usable (like we did with the <a href="https://web.archive.org/web/20211030225402/https://github.com/Transkribus/TranskribusPyClient">Transkribus client</a> in the <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2114">last episode</a>). You can find the improved script <code>cleaner.py</code>on <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/master/cleaner.py">Github</a> along with an extensive <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/master/replacement_table.tsv">replacement table</a> that has grown over the last two years and is still work in progress.</p> <h2>The need for a Latin dictionary</h2> <p>The <code>.replace_abbreviations()</code> function of the <code>Cleaner</code> class can search for patterns and replace them, but it cannot resolve the macrons properly. While the <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/3dd6caf20bef0dfe2db0e3d3f2882ba9dfc5098a/simple_regex.py#L15">hard-coded rule in <code>simple_regex.py</code></a> correctly resolves “ū” in “Christū” as “um”, it would produce an error in “nūc” which should be “nunc” and not “numc”. The easiest way to overcome this problem would be to look up both possibile solutions in a dictionary, i.e. a list of all Latin words including all inflected forms. Then we could verify “nunc” and “Christum” as the correct forms and discard “numc” and “Christun”. Digital Latin dictionaries normally include only the base forms of the words (like <a href="https://web.archive.org/web/20211030225402/http://www.zeno.org/Georges-1913">Karl E. Georges: Ausführliches lateinisch-deutsches Handwörterbuch</a>), but some can handle inflected forms very well (like <a href="https://web.archive.org/web/20211030225402/https://www.navigium.de/latein-woerterbuch.html">Navigium</a>, <a href="https://web.archive.org/web/20211030225402/https://en.wikipedia.org/wiki/William_Whitaker%27s_Words">William Whitaker’s</a> <a href="https://web.archive.org/web/20211030225402/https://archives.nd.edu/words.html">Words</a><sup><a id="fnr.2" class="footref" href="#fn.2">2</a></sup> or a <a href="https://web.archive.org/web/20211030225402/https://extensions.libreoffice.org/en/extensions/show/latin-spelling-and-hyphenation-dictionaries">Latin Hunspell dictionary by Karl Zeiler and Jean-Pierre Sutto</a>).</p> <p>Theoretically, we could use on of these online dictionaries for our purpose. Though, sending an HTTP request needs a lot of time (about 700 milliseconds for Whitaker’s Words and more than one second for Navigium), and we would run into problems sending hundreds of requests to the dictionary’s server. Fortunately, you can <a href="https://web.archive.org/web/20211030225402/http://archives.nd.edu/whitaker/words.htm#D">download Whitaker’s Words</a><sup><a id="fnr.3" class="footref" href="#fn.3">3</a></sup> as a command line program to speed things up. However, you need to write some code (using <a href="https://web.archive.org/web/20211030225402/https://docs.python.org/3/library/subprocess.html">Python’s <code>subprocess</code> module</a>) to make use of the command line program in your Python script. With this approach, it takes about four seconds to check 50 words. This is pretty slow, although the results are quite good because Whitaker’s Words knows many proper names and can handle medieval spelling. More efficient would be to integrate a Latin Hunspell dictionary using the <a href="https://web.archive.org/web/20211030225402/https://pypi.org/project/cyhunspell/"><code>cyhunspell</code> package</a>, which takes only two milliseconds for 50 words, but the results are worse because Hunspell does not know proper names like “Andreas” and complains about non-classical spelling (“columpna” instead of “columna”, or “tercium” for “tertium”). Nevertheless, we will use the Hunspell solution for now.</p> <p>Compared to Whitaker’s Words, using <code>cyhunspell</code> is relatively simple: First, install <code>cyhunspell</code> via <code>pypi</code> as described in the <a href="https://web.archive.org/web/20211030225402/https://pypi.org/project/cyhunspell/">documentation</a>. Then, download the Latin Hunspell dictionary by Karl Zeiler and Jean-Pierre Sutto, not as one <code>.oxt</code> file as provided <a href="https://web.archive.org/web/20211030225402/https://extensions.libreoffice.org/en/extensions/show/latin-spelling-and-hyphenation-dictionaries">by the authors</a>, but as two single files (an <code>.aff</code> and a <code>.dic</code> file) which you can find on <a href="https://web.archive.org/web/20211030225402/https://latin-dict.github.io/docs/hunspell-la.zip">Github</a>. Unzip the downloaded <code>.zip</code> file in your project folder, tell your Python script about the path to the dictionary folder, and you’re ready to go:</p> <pre style="color: #d1d1d1;background: #000000"><span style="color: #e66170;font-weight: bold">from</span> hunspell <span style="color: #e66170;font-weight: bold">import</span> Hunspell <span style="color: #9999a9"># Initialize the dictionary object. hunspell_data_dir is the</span> <span style="color: #9999a9"># directory of the two dictionary files "la_LA.aff" and "la_LA.dic":</span> h <span style="color: #d2cd86">=</span> Hunspell<span style="color: #d2cd86">(</span><span style="color: #00c4c4">'la_LA'</span><span style="color: #d2cd86">,</span> hunspell_data_dir<span style="color: #d2cd86">=</span><span style="color: #00c4c4">'hunspell-la'</span><span style="color: #d2cd86">)</span> <span style="color: #9999a9"># Check a list of words:</span> words <span style="color: #d2cd86">=</span> <span style="color: #d2cd86">[</span><span style="color: #00c4c4">"amamus"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"credo"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"asdf"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"wrong!"</span><span style="color: #d2cd86">,</span> <span style="color: #00c4c4">"spes"</span><span style="color: #d2cd86">]</span> <span style="color: #e66170;font-weight: bold">for</span> word <span style="color: #e66170;font-weight: bold">in</span> words<span style="color: #d2cd86">:</span> <span style="color: #e66170;font-weight: bold">print</span><span style="color: #d2cd86">(</span>word<span style="color: #d2cd86">,</span> h<span style="color: #d2cd86">.</span>spell<span style="color: #d2cd86">(</span>word<span style="color: #d2cd86">)</span><span style="color: #d2cd86">)</span> </pre> <p>Listing 2: <code>simple_hunspell.py</code> on <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/master/simple_hunspell.py">Github</a>.</p> <p>Once again, it is more convenient to wrap all the logic in a class named <code>Dictionary</code> that initializes the <code>Hunspell</code> object and provides a <code>.check_word()</code> function which returns <code>True</code> if the word exists. See <code>dictionary.py</code> on <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/master/dictionary.py">Github</a>.</p> <h2>Resolving macrons</h2> <p>Using the <code>Dictionary()</code> class, we can build a function which resolves macrons properly: First, it has to find all the macrons in a word plus the corresponding vocals. Then, it has to build a list of all possible solutions. Thirdly, the dictionary checks whether the words in the list exist or not. If so, it returns the word as the solution. Otherwise, it replaces the macron with a dot “●” so that we can easily spot unresolved macrons in the text. See the <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/3dd6caf20bef0dfe2db0e3d3f2882ba9dfc5098a/cleaner.py#L101-L141"><code>.replace_macrons()</code> function</a> of the <code>Cleaner</code> class.</p> <p>Example: “Christū”</p> <ol> <li>Find macrons and vocals: macron: “ū”, vocal: “u”.</li> <li>List of possible solutions: <code>[”Christun”, ”Christum”]</code>.</li> <li>The dictionary says: “Christun” = <code>False</code>, “Christum” = <code>True</code>.</li> <li>Return “Christum”.</li> </ol> <h2>Tokenizing the text</h2> <p>Since the dictionary can only process one word at a time, we have to feed it with single words. That means, before we call the <code>.replace_macrons()</code> function, we have to split each line of text into individual words. The process of splitting a stream of data into separate parts (or “tokens”) is called “tokenization”.</p> <p>Again, regular expressions can help us to solve this problem. In Python, the built-in <code>re</code> module provides a <code>re.split()</code> function that does most of the job in one line: <code>re.split("([\s.,;:?\-=()\]])", text)</code> splits the text at each whitespace (<code>\s</code>) and each punctuation (<code>.,;:?-=()]</code>, note that “-” and “]” must be preceded by a backslash so they are not interpreted as metacharacters). Some post-processing decides whether the token is a normal word or punctuation. The <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/3dd6caf20bef0dfe2db0e3d3f2882ba9dfc5098a/cleaner.py#L47-L77"><code>.tokenize()</code> function</a> returns a list containing the tokenized text, which can be fed to the <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/3dd6caf20bef0dfe2db0e3d3f2882ba9dfc5098a/cleaner.py#L79-L141"><code>.resolve_macrons()</code> function</a>. In the end, we will get each line of text as a list of word objects (Python dictionaries) containing the normalized words and the information whether it is a normal word or punctuation.</p> <h2>Resolve hyphens and line breaks</h2> <p>There remains one last problem: hyphenation and line breaks. As we have seen above, there is not always a hyphen when a word is split at the end of a line.</p> <ol> <li>Even when there is a hyphen, resolving line breaks is surprisingly complicated: Thanks to the hyphen we do not need to consult a dictionary before joining the two tokens, but we cannot resolve the macron if we consider only the first element. In “cō- | scientia”, “cō” only makes sense in combination with “scientia”, so we should run the <code>.replace_macrons()</code> function on the combined string “cōscientia” after having resolved hyphenations and line breaks. Joining the tokens means that the hyphen after “cō” is dropped and that “scientia” moves from the beginning of the second line to the end of the first line.</li> <li>If the first line ends with punctuation other than “-” or “=”, the tokens do not need to be joined, but we have to resolve the macrons for both tokens separately.</li> <li>The same is applies if the second line begins with a capital letter.</li> <li>If the hyphen is missing we try to join both tokens and verify the word in the dictionary. If the word does not exist, we proceed as in 2).</li> <li>If it does exist, we move the second token to the end of the first line as in example 1). In case the second token in the second line is punctuation, the second token must also be moved to the end of the first line. Moving tokens to the first line sometimes results in an empty second line. In this case, we should flag the second line as “empty”.</li> </ol> <figure id="attachment_2159" aria-describedby="caption-attachment-2159" style="width: 500px" class="wp-caption alignnone"><a href="https://web.archive.org/web/20211030225402/http://dhlab.hypotheses.org/files/2021/09/transkribus_3_resolve_line_breaks.jpg"><img loading="lazy" class="wp-image-2159 size-large" src="https://web.archive.org/web/20211030225402im_/http://dhlab.hypotheses.org/files/2021/09/transkribus_3_resolve_line_breaks-500x331.jpg" alt="" width="500" height="331" srcset="https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_resolve_line_breaks-500x331.jpg 500w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_resolve_line_breaks-300x199.jpg 300w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_resolve_line_breaks-768x509.jpg 768w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_resolve_line_breaks.jpg 969w" sizes="(max-width: 500px) 85vw, 500px"/></a><figcaption id="caption-attachment-2159" class="wp-caption-text">Resolving linebreaks is surprisingly complicated.</figcaption></figure> <p>This complicated logic is implemented in two functions of the <code>Cleaner</code> class: <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/3dd6caf20bef0dfe2db0e3d3f2882ba9dfc5098a/cleaner.py#L143-L228"><code>.resolve_linebreaks()</code></a> decides whether to join the two tokens or not, <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/3dd6caf20bef0dfe2db0e3d3f2882ba9dfc5098a/cleaner.py#L238-L296"><code>.join_words()</code></a> combines the words if necessary and normalizes them including the macrons.</p> <h2>The normalization pipeline</h2> <p>We now have all the building blocks for a complete normalization pipeline. First, we download the raw text using the <code>Transkribus_Web</code> client developed in <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2114">episode II</a>. Then, the <code>Cleaner</code> class does its job: <code>.replace_abbreviations()</code> uses the replacement table to normalize most of the abbreviations. The third step is tokenization using a regular expression. The tokenized lines are ready for resolving line breaks in the fourth step. Here and when we finally resolve the macrons, we need to consult a Latin dictionary.</p> <figure id="attachment_2160" aria-describedby="caption-attachment-2160" style="width: 500px" class="wp-caption alignnone"><a href="https://web.archive.org/web/20211030225402/http://dhlab.hypotheses.org/files/2021/09/transkribus_3_normalization_pipeline.jpg"><img loading="lazy" class="wp-image-2160 size-large" src="https://web.archive.org/web/20211030225402im_/http://dhlab.hypotheses.org/files/2021/09/transkribus_3_normalization_pipeline-500x375.jpg" alt="" width="500" height="375" srcset="https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_normalization_pipeline-500x375.jpg 500w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_normalization_pipeline-300x225.jpg 300w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_normalization_pipeline-768x576.jpg 768w, https://web.archive.org/web/20211030225402im_/https://dhlab.hypotheses.org/files/2021/09/transkribus_3_normalization_pipeline.jpg 1036w" sizes="(max-width: 500px) 85vw, 500px"/></a><figcaption id="caption-attachment-2160" class="wp-caption-text">A normalization pipeline for raw Latin text.</figcaption></figure> <h2>A simple command line interface</h2> <p>All the functions created in this episode are wrapped in classes (<code>Cleaner</code>, <code>Dictionary</code>, <code>IO_Tools</code>) which live in different <code>.py</code> files (<code>cleaner.py</code>, <code>dictionary.py</code>, <code>tools.py</code>). Splitting the code into different files makes it more maintainable and helps to keep the overview. The classes in these files can be imported as modules, e.g. by saying <code>from cleaner import Cleaner</code>, meaning “use the <code>Cleaner</code> class from<code> cleaner.py</code> in this script”. The only thing missing is a main function from where we can operate the whole machine.</p> <p>For now, a very simple command line interface should do the job: The <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/3dd6caf20bef0dfe2db0e3d3f2882ba9dfc5098a/cli.py#L9-L156"><code>Transkribus_CLI</code> class</a> in <code>cli.py</code> organizes the communication between the user and the various parts of the software. <code>cli.login()</code> initializes the <code><a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/transkribus_rest_api_client">Transkribus_Web</a></code> client (written in <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2114">episode I</a>) and logs the user in to the Transkribus server. <code>cli.choose_collection()</code> lets the user choose a collection from a list of all available collections. Using the same logic, the user can then to select a document and a page. Finally, <code>cli.print_page()</code> downloads the raw text (the diplomatic transcription) from Transkribus, feeds it to the normalization pipeline, and prints it to the screen. After that, we say good bye with <code>cli.logout()</code>.</p> <pre style="color: #d1d1d1;background: #000000"><span style="color: #e66170;font-weight: bold">from</span> cli <span style="color: #e66170;font-weight: bold">import</span> Transkribus_CLI cli <span style="color: #d2cd86">=</span> Transkribus_CLI<span style="color: #d2cd86">(</span><span style="color: #d2cd86">)</span> cli<span style="color: #d2cd86">.</span>login<span style="color: #d2cd86">(</span><span style="color: #d2cd86">)</span> colId <span style="color: #d2cd86">=</span> cli<span style="color: #d2cd86">.</span>choose_collection<span style="color: #d2cd86">(</span><span style="color: #d2cd86">)</span> docId <span style="color: #d2cd86">=</span> cli<span style="color: #d2cd86">.</span>choose_document<span style="color: #d2cd86">(</span>colId<span style="color: #d2cd86">)</span> pageNr <span style="color: #d2cd86">=</span> cli<span style="color: #d2cd86">.</span>choose_page<span style="color: #d2cd86">(</span>colId<span style="color: #d2cd86">,</span> docId<span style="color: #d2cd86">)</span> page <span style="color: #d2cd86">=</span> cli<span style="color: #d2cd86">.</span>get_page<span style="color: #d2cd86">(</span>colId<span style="color: #d2cd86">,</span> docId<span style="color: #d2cd86">,</span> pageNr<span style="color: #d2cd86">)</span> cli<span style="color: #d2cd86">.</span>print_page<span style="color: #d2cd86">(</span>page<span style="color: #d2cd86">,</span> raw_text<span style="color: #d2cd86">=</span>False<span style="color: #d2cd86">)</span> cli<span style="color: #d2cd86">.</span>logout<span style="color: #d2cd86">(</span><span style="color: #d2cd86">)</span> </pre> <p>Listing 4: The <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/master/cli.py"><code>Transkribus_CLI</code> class</a> provides a rudimentary command line interface. Using this CLI, the <a href="https://web.archive.org/web/20211030225402/https://github.com/gedoensmanagement/resolve_abbreviations/blob/master/normalizer.py"><code>normalizer.py</code> script</a> selects collections, documents and pages on the Transkribus server, downloads the raw text from Transkribus, normalizes it, and prints it to the screen.</p> <p>This rudimentary user interface is fine for demonstration and testing purposes, but it is too clumsy for working efficiently. Therefore, we will give it a thorough overhaul in the next episode and build a user-friendly web interface. In addition, we will make use of the Latin dictionary to detect OCR transcription errors and correct them semi-automatically.</p> <hr/> <p><em>Footnotes</em></p> <p><sup><a id="fn.1" href="#fnr.1">1</a></sup> The Ad fontes learning program of the University of Zurich offers a very nice <a href="https://web.archive.org/web/20211030225402/https://www.adfontes.uzh.ch/en/tutorium/einleitung">introduction to palaeography</a> and <a href="https://web.archive.org/web/20211030225402/https://www.adfontes.uzh.ch/en/3001/training/einleitung">exercises</a>; <a href="https://web.archive.org/web/20211030225402/https://en.wikipedia.org/wiki/Scribal_abbreviation">Wikipedia</a> provides some basic information; <a href="https://web.archive.org/web/20211030225402/https://transkribus.eu/r/learn/">Transkribus</a> has its own training section; for the peculiarities of English palaeography see the <a href="https://web.archive.org/web/20211030225402/https://www.nationalarchives.gov.uk/palaeography/">National Archives</a>.</p> <p><sup><a id="fn.2" href="#fnr.2">2</a></sup> Cf. extensive documentation on Whitaker’s Words: <a href="https://web.archive.org/web/20211030225402/http://archives.nd.edu/words.htm">http://archives.nd.edu/words.htm</a>.</p> <p><sup><a id="fn.3" href="#fnr.3">3</a></sup> Find the source code of Whitaker’s Words on Github <a href="https://web.archive.org/web/20211030225402/https://github.com/mk270/whitakers-words">here</a> or <a href="https://web.archive.org/web/20211030225402/https://github.com/dsanson/Words">here</a>. There is also a <a href="https://web.archive.org/web/20211030225402/https://github.com/ArchimedesDigital/open_words">port from Ada code to Python</a>.</p> <div class="wpcp">Cite this article as: Markus Müller, "Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text," in <em>Digital Humanities Lab</em>, 03/09/2021, <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2154">https://dhlab.hypotheses.org/2154</a>.</div> <hr/> <p style="line-height: 1.1"><span style="font-size: 10pt">Featured Image: <em>Let Python Talk to Transkribus</em>, 2021, by Markus Müller, <a href="https://web.archive.org/web/20211030225402/https://creativecommons.org/licenses/by-sa/4.0/deed.en">CC BY-SA 4.0</a>,<br/> build upon <a href="https://web.archive.org/web/20211030225402/https://commons.wikimedia.org/wiki/File:Wolpertinger.jpg">Wolpertinger</a> by Rainer Zenz, <a href="https://web.archive.org/web/20211030225402/https://creativecommons.org/licenses/by-sa/3.0/deed.en">CC BY-SA 3.0</a>; <a href="https://web.archive.org/web/20211030225402/https://commons.wikimedia.org/wiki/File:Python_natalensis_Smith_1840_white_background.jpg">Python natalensis</a>, in: Andrew Smith: Illustrations of the zoology of South Africa, Reptilia. Smith, Elder, and Co., London 1840, PD; Markus Müller: Sammelband mit Schriften Johann Wilds, Wissenschaftliche Stadtbibliothek Mainz, Signatur 559 q 1; <a href="https://web.archive.org/web/20211030225402/https://commons.wikimedia.org/wiki/File:%27Haunted_table%27_of_The_Black_Horse_Inn,_Nuthurst,_West_Sussex.jpg">The ‘haunted table’ in the bar of the Black Horse Inn public house</a>, on Nuthurst Street in Nuthurst, West Sussex, England, by Acabashi, <a href="https://web.archive.org/web/20211030225402/https://creativecommons.org/licenses/by-sa/4.0/deed.en">CC BY-SA 4.0</a>.</span></p> <hr/>  <ul class="soc"> <li><a class="twitter share-hypotheses soc-twitter" href="https://web.archive.org/web/20211030225402/https://twitter.com/share?text=Uncovering+censorship+in+the+16th+century+with+Transkribus+and+Python.+Episode+III%3A+Normalizing+16th+century+raw+text&url=https%3A%2F%2Fdhlab.hypotheses.org%2F2154&via=hypothesesorg"></a></li> <li><a class="facebook share-hypotheses soc-facebook" href="https://web.archive.org/web/20211030225402/https://www.facebook.com/sharer.php?title=Uncovering+censorship+in+the+16th+century+with+Transkribus+and+Python.+Episode+III%3A+Normalizing+16th+century+raw+text&u=https%3A%2F%2Fdhlab.hypotheses.org%2F2154"></a></li> <li><a class="linkedin share-hypotheses soc-linkedin" href="https://web.archive.org/web/20211030225402/https://www.linkedin.com/shareArticle?title=Uncovering+censorship+in+the+16th+century+with+Transkribus+and+Python.+Episode+III%3A+Normalizing+16th+century+raw+text&url=https%3A%2F%2Fdhlab.hypotheses.org%2F2154&mini=true"></a></li> </ul> <div class="author-info"> <div class="author-avatar"> <img alt="" src="https://web.archive.org/web/20211030225402im_/https://secure.gravatar.com/avatar/03e7503b07a7704c7483675902117e89?s=42&d=blank&r=g" srcset="https://web.archive.org/web/20211030225402im_/https://secure.gravatar.com/avatar/03e7503b07a7704c7483675902117e89?s=84&d=blank&r=g 2x" class="avatar avatar-42 photo" height="42" width="42" loading="lazy"/> </div> <div class="author-description"> <h2 class="author-title"><span class="author-heading">Autor:</span> Markus Müller</h2> <p class="author-bio"> Studied Catholic theology and education science in Tübingen (2000–2006) and Salamanca (2002/2003). 2012: PhD in Catholic theology (ecclesiastical history) in Tübingen. Research assistant in Tübingen, assistant lecturer in Frankfurt and Mainz. Since 2018, postdoctoral researcher at the Institute of European History in Mainz. – Research interests: history of religious education, censorship in the 16th century, Digital Humanities. <a class="author-link" href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/author/muellerm" rel="author"> Alle Beiträge von Markus Müller anzeigen </a> </p> </div> </div> </div> <footer class="entry-footer"> <span class="byline"><span class="author vcard"><img alt="" src="https://web.archive.org/web/20211030225402im_/https://secure.gravatar.com/avatar/03e7503b07a7704c7483675902117e89?s=49&d=blank&r=g" srcset="https://web.archive.org/web/20211030225402im_/https://secure.gravatar.com/avatar/03e7503b07a7704c7483675902117e89?s=98&d=blank&r=g 2x" class="avatar avatar-49 photo" height="49" width="49" loading="lazy"/><span class="screen-reader-text">Autor </span> <a class="url fn n" href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/author/muellerm">Markus Müller</a></span></span><span class="posted-on"><span class="screen-reader-text">Veröffentlicht am </span><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2154" rel="bookmark"><time class="entry-date published" datetime="2021-09-03T11:00:52+02:00">03/09/2021</time><time class="updated" datetime="2021-10-29T11:29:46+02:00">29/10/2021</time></a></span><span class="cat-links"><span class="screen-reader-text">Kategorien </span><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/category/lab-report" rel="category tag">Lab Report</a></span><span class="tags-links"><span class="screen-reader-text">Schlagwörter </span><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/tag/digital-humanities" rel="tag">digital humanities</a>, <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/tag/ocr" rel="tag">OCR</a>, <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/tag/programming" rel="tag">programming</a>, <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/tag/python" rel="tag">Python</a>, <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/tag/regular-expressions" rel="tag">Regular Expressions</a>, <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/tag/transkribus" rel="tag">Transkribus</a></span> </footer> </article> <div id="comments" class="comments-area"> <h2 class="comments-title"> 3 Gedanken zu „Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text“ </h2> <ol class="comment-list"> <li id="comment-271" class="pingback even thread-even depth-1"> <div class="comment-body"> Pingback: <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2114" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. Episode II: Let Python speak to Transkribus – Digital Humanities Lab</a> </div> </li> <li id="comment-270" class="pingback odd alt thread-odd thread-alt depth-1"> <div class="comment-body"> Pingback: <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2022" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. Episode I: OCR with Latin prints – Digital Humanities Lab</a> </div> </li> <li id="comment-269" class="pingback even thread-even depth-1"> <div class="comment-body"> Pingback: <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2191" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. 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Episode V: How did the censors actually change the text?</a> <span class="post-date">29/10/2021</span> </li> <li> <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2251">„Experiment, Ergebnis, Erkenntnis“ – Dokumentation einer Interviewreihe zu Projektmanagement in den Digital Humanities</a> <span class="post-date">15/10/2021</span> </li> <li> <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2219">Geohumanities III: analysing early modern mobility through birth and apprenticeship letters</a> <span class="post-date">01/10/2021</span> </li> </ul> </nav></section><section id="recent-comments-3" class="widget widget_recent_comments"><h2 class="widget-title">Comments</h2><nav role="navigation" aria-label="Comments"><ul id="recentcomments"><li class="recentcomments"><span class="comment-author-link"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2114" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. Episode II: Let Python speak to Transkribus – Digital Humanities Lab</a></span> bei <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2154#comment-271">Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text</a></li><li class="recentcomments"><span class="comment-author-link"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2022" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. Episode I: OCR with Latin prints – Digital Humanities Lab</a></span> bei <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2154#comment-270">Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text</a></li><li class="recentcomments"><span class="comment-author-link"><a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2191" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. Episode IV: Detecting OCR transcription errors – Digital Humanities Lab</a></span> bei <a href="https://web.archive.org/web/20211030225402/https://dhlab.hypotheses.org/2154#comment-269">Uncovering censorship in the 16th century with Transkribus and Python. 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