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Manual Ergänzungen Intro /Getting Started
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<h3 class="manual-chapter-title">Introduction</h3>
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<h4>Introduction</h4>
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<p>
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nopaque is a web-based digital working environment. It implements a
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workflow based on the research process in the humanities and supports its
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users in processing their data in order to subsequently apply digital
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analysis methods to them. All processes are implemented in a specially
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provided cloud environment with established open source software. This
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always ensures that no personal data of the users is disclosed.
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Nopaque is a web application that offers different services and tools to support
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researchers working with image and text-based data. These services are logically
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connected and build upon each other. They include:
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</p>
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<ol style="list-style-type:disc; margin-left:2em; padding-bottom:0;">
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<li><b>File setup</b>, which converts and merges different data (e.g., books, letters)
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for further processing.</li>
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<li><b>Image-to-text conversion tools:</b></li>
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<ol style="list-style-type:circle; margin-left:1em; padding-bottom:0;"><li><b>Optical Character Recognition</b> converts photos and
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scans into text data, making them machine-readable.</li>
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<li><b>Transkribus HTR (Handwritten Text Recognition) Pipeline</b>
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also converts images into text data, making them machine-readable.</li>
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</ol>
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<li><b>Natural Language Processing</b> extracts information from your text via
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computational linguistic data processing (tokenization, lemmatization, part-of-speech
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tagging and named-entity recognition.</li>
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<li><b>Corpus analysis</b> makes use of CQP Query Language to search through text
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corpora with the aid of metadata and Natural Language Processing tags.</li>
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</ol>
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Nopaque also features a <b>Social Area</b>, where researchers can create a personal profile, connect with other users and share corpora if desired.
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These services can be accessed from the sidebar in nopaque.
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All processes are implemented in a specially provided cloud environment with established open-source software. This always ensures that no personal data of the users is disclosed.
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<h3 class="manual-chapter-title">Registration and Log in</h3>
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<div class="row">
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<div class="col s12 m4">
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<img alt="Registration and Log in" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/registration-and-log-in.png') }}">
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</div>
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<div class="col s12 m8">
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<p>
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Before you can start using the web platform, you need to create a user
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account. This requires only a few details: just a user name, an e-mail
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address and a password are needed. In order to register yourself, fill out
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the form on the <a href="{{ url_for('auth.register') }}">registration page</a>. After successful registration, the
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created account must be verified. To do this, follow the instructions
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given in the automatically sent e-mail. Afterwards, you can log in as
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usual with your username/email address and password in the log-in form
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located next to the registration button.
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</p>
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</div>
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<h3 class="manual-chapter-title">Getting Started</h3>
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<h4>Getting Started</h4>
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<br>
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<div style="border: 1px solid; padding-left: 20px; margin-right: 400px; margin-bottom: 40px;">
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<h5>Content</h5>
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<ol style="list-style-type:disc">
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<li><a href="#registration-and-login">Registration and login</a></li>
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<li><a href="#preparing-files">Preparing files for analysis</a></li>
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<li><a href="#converting-a-pdf-into-text">Converting a PDF into text data</a></li>
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<li><a href="#extracting-linguistic-data">Extracting linguistic data from text</a></li>
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<li><a href="#creating-a-corpus">Creating a corpus</a></li>
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<li><a href="#analyzing-a-corpus">Analyzing a corpus</a></li>
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</ol>
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</div>
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<p></p>
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<h5 id="registration-and-login">Registration and login</h5>
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<p>Before you can begin using nopaque, you will need to create a personal user account.
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Open the menu (three dots) at the top right of the screen and choose “Register.” Enter
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the required details listed on the registration page (username, password, email address).
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After verifying your account via the link sent to your email, you can log in.</p>
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<h5 id="preparing-files">Preparing files for analysis</h5>
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<p>A few steps need to be taken before images, scans, or other text data are ready for
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analysis in nopaque. The SpaCy NLP Pipeline service can only extract linguistic data
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from texts in plain text (.txt) format. If your text is already in this format, you
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can skip the next steps and go directly to <b>Extracting linguistic data from text</b>.
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Otherwise, the next steps assume that you are starting off with image data.</p>
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<p>
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First, all data needs to be converted into PDF format. Using the <b>File Setup</b> service,
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you can bundle images together – even of different formats – and convert them all into
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one PDF file. Note that the File Setup service will sort the images based on their file
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name in ascending order. It is thus recommended to name them accordingly, for example:
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page-01.png, page-02.jpg, page-03.tiff.
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</p>
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<p>
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After uploading the images and completing the File Setup job, the list of files added
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can be seen under “Inputs.” Further below, under “Results,” you can find and download
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the PDF output.</p>
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<h5 id="converting-a-pdf-into-text">Converting a PDF into text data</h5>
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<p>Select an image-to-text conversion tool depending on whether your PDF is primarily
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composed of handwritten text or printed text. For printed text, select the <b>Tesseract OCR
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Pipeline</b>. For handwritten text, select the <b>Transkribus HTR Pipeline</b>. Select the desired
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language model or upload your own. Select the version of Tesseract OCR you want to use
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and click on submit to start the conversion. When the job is finished, various output
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files can be seen and downloaded further below, under “Results.” You may want to review
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the text output for errors and coherence.</p>
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<h5 id="extracting-linguistic-data">Extracting linguistic data from text</h5>
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<p>The <b>SpaCy NLP Pipeline</b> service extracts linguistic information from plain text files
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(in .txt format). Select the corresponding .txt file, the language model, and the
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version you want to use. When the job is finished, find and download the files in
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<b>.json</b> and <b>.vrt</b> format under “Results.”</p>
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<h5 id="creating-a-corpus">Creating a corpus</h5>
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<p>Now, using the files in .vrt format, you can create a corpus. This can be done
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in the Dashboard or Corpus Analysis under “My Corpora.” Click on “Create corpus”
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and add a title and description for your corpus. After submitting, navigate down to
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the “Corpus files” section. Once you have added the desired .vrt files, select “Build”
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on the corpus page under “Actions.” Now, your corpus is ready for analysis.</p>
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<h5 id="analyzing-a-corpus">Analyzing a corpus</h5>
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<p>Navigate to the corpus you would like to analyze and click on the Analyze button.
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This will take you to an analysis overview page for your corpus. Here, you can find a
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visualization of general linguistic information of your corpus, including tokens,
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sentences, unique words, unique lemmas, unique parts of speech and unique simple parts
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of speech. You will also find a pie chart of the proportional textual makeup of your
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corpus and can view the linguistic information for each individual text file. A more
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detailed visualization of token frequencies with a search option is also on this page.</p>
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<p>From the corpus analysis overview page, you can navigate to other analysis modules:
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the <b>Query Builder</b> (under <b>Concordance</b>) and the <b>Reader</b>. With the Reader, you can read
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your corpus texts tokenized with the associated linguistic information. The tokens can
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be shown as lemmas, parts of speech, words, and can be displayed in different ways:
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visually as plain text with the option of highlighted entities or as chips.</p>
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<p>The <b>Concordance</b> module allows for more specific, query-oriented text analyses.
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Here, you can filter out text parameters and structural attributes in different
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combinations. This is explained in more detail in the Query Builder section of the
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manual.</p>
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@ -1,15 +1,20 @@
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<h3 class="manual-chapter-title">Dashboard</h3>
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<h4>About the dashboard</h4>
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<br>
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<div class="row">
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<div class="col s12 m4">
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<img alt="Dashboard" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/dashboard.png') }}">
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</div>
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<div class="col s12 m8">
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<p>
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The <a href="{{ url_for('main.dashboard') }}">dashboard</a> provides a central overview of all resources assigned to the
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user. These are <a href="{{ url_for('main.dashboard', _anchor='corpora') }}">corpora</a> and created <a href="{{ url_for('main.dashboard', _anchor='jobs') }}">jobs</a>. Corpora are freely composable
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annotated text collections and jobs are the initiated file processing
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procedures. One can search for jobs as well as corpus listings using
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the search field displayed above them.
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The <a href="{{ url_for('main.dashboard') }}">dashboard</a> provides a central
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overview of all user-specific resources.
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These are <a href="{{ url_for('main.dashboard', _anchor='corpora') }}">corpora</a>,
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created <a href="{{ url_for('main.dashboard', _anchor='jobs') }}">jobs</a>, and
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model contributions.
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A corpus is a freely composable annotated text collection.
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A job is an initiated file processing procedure. One can search for jobs as
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well as corpus listings using the search field displayed above them on the dashboard.
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</p>
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</div>
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<div class="col s12"> </div>
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