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53 lines
2.3 KiB
Plaintext
53 lines
2.3 KiB
Plaintext
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<h2>Services</h2>
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<div class="row">
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<div class="col s12 m4">
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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>Comming soon...</p>
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<h3>Handwritten Text Recognition (HTR)</h3>
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<p>Comming soon...</p>
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<h3>Natural Language Processing (NLP)</h3>
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<p>Comming 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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