nopaque/app/templates/main/_manual/06_services.html.j2

<h2>Services</h2>
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    <img alt="Services" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/services.png') }}">
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    <p>
      nopaque was designed from the ground up to be modular. This modularity
      means that the offered workflow provides variable entry and exit points,
      so that different starting points and goals can be flexibly addressed.
      Each of these modules are implemented in a self-contained service, each of
      which represents a step in the workflow. The services are coordinated in
      such a way that they can be used consecutively. The order can either be
      taken from the listing of the services in the left sidebar or from the
      roadmap (accessible via the pink compass in the upper right corner). All
      services are versioned, so the data generated with nopaque is always
      reproducible.
    </p>
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<h3>File Setup</h3>
<p>
  The <a href="{{ url_for('services.file_setup_pipeline') }}">File Setup Service</a> bundles image data, such as scans and photos,
  together in a handy PDF file. To use this service, use the job form to
  select the images to be bundled, choose the desired service version, and
  specify a title and description. Please note that the service sorts the
  images into the resulting PDF file based on the file names. So naming the
  images correctly is of great importance. It has proven to be a good practice
  to name the files according to the following scheme:
  page-01.png, page-02.jpg, page-03.tiff, etc. In general, you can assume
  that the images will be sorted in the order in which the file explorer of
  your operating system lists them when you view the files in a folder
  sorted in ascending order by file name.
</p>

<h3>Optical Character Recognition (OCR)</h3>
<p>Coming soon...</p>

<h3>Handwritten Text Recognition (HTR)</h3>
<p>Coming soon...</p>

<h3>Natural Language Processing (NLP)</h3>
<p>Coming soon...</p>

<h3>Corpus Analysis</h3>
<p>
  With the corpus analysis service, it is possible to create a text corpus
  and then explore it in an analysis session. The analysis session is realized
  on the server side by the Open Corpus Workbench software, which enables
  efficient and complex searches with the help of the CQP Query Language.
</p>

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    <img alt="Create a Corpus" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/create-a-corpus.png') }}">
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    <p>
      To <a href="{{ url_for('corpora.create_corpus') }}">create a corpus</a>, you
      can use the "New Corpus" button, which can be found on both the Corpus
      Analysis Service page and the Dashboard below the corpus list. Fill in the input
      mask to Create a corpus. After you have completed the input mask, you will
      be automatically taken to the corpus overview page (which can be called up
      again via the corpus lists) of your new and accordingly still empty corpus.
    </p>
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    <img alt="Create a Corpus" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/add-corpus-file.png') }}">
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    <p>
      Now you can add texts in vrt format (results of the NLP service) to your new
      corpus. To do this, use the "Add Corpus File" button and fill in the form
      that appears. You will get the possibility to add metadata to each text.
      After you have added all the desired texts to the corpus, the corpus must be
      prepared for the analysis, this process can be initiated by clicking on the
      "Build" button. On the corpus overview page you can always see information
      about the current status of the corpus in the upper right corner. After the
      build process the status should be "built".
    </p>
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<h4>Analyze a corpus</h4>
<p>
  After you have created and built a corpus, it can be analyzed. To do this,
  use the button labeled Analyze. The corpus analysis currently offers two
  modules, the Reader and the Concordance module. The reader module can be
  used to read your tokenized corpus in different ways. You can select a token
  representation option, it determines the property of a token to be shown.
  You can for example read your text completly lemmatized. You can also change
  the way of how a token is displayed, by using the text style switch. The
  concordance module offers some more options regarding the context size of
  search results. If the context does not provide enough information you can
  hop into the reader module by using the magnifier icon next to a match.
</p>