mirror of
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107 lines
4.8 KiB
Django/Jinja
107 lines
4.8 KiB
Django/Jinja
<h2>Services</h2>
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<div class="row">
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<div class="col s12 m4">
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<br class="hide-on-small-only">
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<img alt="Services" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/services.png') }}">
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</div>
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<div class="col s12 m8">
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<p>
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nopaque was designed from the ground up to be modular. This modularity
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means that the offered workflow provides variable entry and exit points,
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so that different starting points and goals can be flexibly addressed.
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Each of these modules are implemented in a self-contained service, each of
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which represents a step in the workflow. The services are coordinated in
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such a way that they can be used consecutively. The order can either be
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taken from the listing of the services in the left sidebar or from the
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roadmap (accessible via the pink compass in the upper right corner). All
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services are versioned, so the data generated with nopaque is always
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reproducible.
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</p>
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</div>
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</div>
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<h3>File Setup</h3>
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<p>
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The <a href="{{ url_for('services.file_setup_pipeline') }}">File Setup Service</a> bundles image data, such as scans and photos,
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together in a handy PDF file. To use this service, use the job form to
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select the images to be bundled, choose the desired service version, and
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specify a title and description. Please note that the service sorts the
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images into the resulting PDF file based on the file names. So naming the
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images correctly is of great importance. It has proven to be a good practice
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to name the files according to the following scheme:
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page-01.png, page-02.jpg, page-03.tiff, etc. In general, you can assume
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that the images will be sorted in the order in which the file explorer of
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your operating system lists them when you view the files in a folder
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sorted in ascending order by file name.
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</p>
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<h3>Optical Character Recognition (OCR)</h3>
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<p>Coming soon...</p>
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<h3>Handwritten Text Recognition (HTR)</h3>
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<p>Coming soon...</p>
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<h3>Natural Language Processing (NLP)</h3>
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<p>Coming soon...</p>
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<h3>Corpus Analysis</h3>
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<p>
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With the corpus analysis service, it is possible to create a text corpus
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and then explore it in an analysis session. The analysis session is realized
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on the server side by the Open Corpus Workbench software, which enables
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efficient and complex searches with the help of the CQP Query Language.
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</p>
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<div class="row">
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<div class="col s12 m4">
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<br class="hide-on-small-only">
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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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</div>
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<div class="col s12 m8">
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<p>
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To <a href="{{ url_for('corpora.create_corpus') }}">create a corpus</a>, you
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can use the "New Corpus" button, which can be found on both the Corpus
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Analysis Service page and the Dashboard below the corpus list. Fill in the input
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mask to Create a corpus. After you have completed the input mask, you will
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be automatically taken to the corpus overview page (which can be called up
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again via the corpus lists) of your new and accordingly still empty corpus.
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</p>
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</div>
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</div>
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<div class="row">
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<div class="col s12 m4">
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<br class="hide-on-small-only">
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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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</div>
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<div class="col s12 m8">
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<p>
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Now you can add texts in vrt format (results of the NLP service) to your new
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corpus. To do this, use the "Add Corpus File" button and fill in the form
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that appears. You will get the possibility to add metadata to each text.
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After you have added all the desired texts to the corpus, the corpus must be
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prepared for the analysis, this process can be initiated by clicking on the
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"Build" button. On the corpus overview page you can always see information
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about the current status of the corpus in the upper right corner. After the
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build process the status should be "built".
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</p>
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</div>
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</div>
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<h4>Analyze a corpus</h4>
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<p>
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After you have created and built a corpus, it can be analyzed. To do this,
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use the button labeled Analyze. The corpus analysis currently offers two
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modules, the Reader and the Concordance module. The reader module can be
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used to read your tokenized corpus in different ways. You can select a token
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representation option, it determines the property of a token to be shown.
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You can for example read your text completly lemmatized. You can also change
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the way of how a token is displayed, by using the text style switch. The
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concordance module offers some more options regarding the context size of
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search results. If the context does not provide enough information you can
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hop into the reader module by using the magnifier icon next to a match.
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</p>
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