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title = "Tutorial"
sort_by = "weight"
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-
-# This is only available in draft mode
-draft = true
+insert_anchor_links = "right"
+++
This documentation is aimed at Digital Humanities specialists who want to transcribe handwritten or printed documents using our Arkindex platform.
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+++
-title = "Corpus identification"
+title = "Corpus import"
weight = 20
-draft = true
+++
-- example https://europeana.transcribathon.eu/documents/story/item/?item=1258824
-- french, completed transcription, handwritten
-- import inside arkindex for segmentation + transcription
\ No newline at end of file
+In this tutorial, you will learn how to import images and metadata in Arkindex.
+
+## Corpus description
+
+As an example, you will import the [**Pellet**](https://europeana.transcribathon.eu/documents/story/?story=121795) dataset from the Europeana 1914-1918 collection.
+
+The corpus contains 471 scanned documents related to Casimir Marius PELLET, a French soldier during World War I.
+
+The documents are written in French and include various content types, such as campaign diaries, photographs, and postcards. Each document has been transcribed by volunteers and includes descriptive metadata.
+We have selected this corpus as it covers a large variety of document, while still being relatively small to avoid complexity due to large annotation and training needs.
+
+{{ figure(image="tutorial/corpus.jpg", height=200, caption="Documents from the Pellet corpus") }}
+Of course you may import your own data directly in Arkindex, using file uploads. Arkindex supports images, PDFs, METS, ALTO, ZIP archive compatible with Transkribus, ...
+
+## Create a project in Arkindex
+
+{% info() %}
+This section expects you to have an Arkindex account. Learn how to register [here](@/users/auth/index.md).
+{% end %}
+
+Log in to [Arkindex](https://demo.arkindex.org/) by entering your email and password.
+
+On the front page, you will find an empty corpus entitled `My Project`. We will publish the data from Europeana in this corpus. Alternatively, you can create a new project by clicking on the `New Project` button at the top right of the page. Note that this corpus is personal and can only be accessed by you.
+
+To edit your project name and and description:
+* Click on `My Project`
+{{ figure(image="tutorial/arkindex_select_project.jpg", height=300, caption="Select your project") }}
+* Go on your project information page
+{{ figure(image="tutorial/arkindex_project_information.jpg", height=200, caption="Go to your project information page") }}
+* Edit your project's name and description and click on `Update`
+ * **Name**: `Europeana | Pellet`
+ * **Description**: `Corpus from [Europeana](https://europeana.transcribathon.eu/documents/story/?story=121795)`
+{{ figure(image="tutorial/arkindex_edit_project.jpg", height=300, caption="Edit your project's name and description") }}
+
+## Import data to Arkindex
+
+Two steps are required to import the corpus in Arkindex:
+1. Extract the data from Europeana (images, transcriptions and metadata)
+2. Publish it to your Arkindex project
+
+{% info() %}
+You will need Python 3.10 and a shell environment (we recommend Ubuntu or Mac OS X)
+{% end %}
+
+We have released a Python package named [`arkindex-scrapers`](https://pypi.org/project/arkindex-scrapers/) to help you achieve these steps. To install it to your environment, run:
+
+```sh
+pip install teklia-scrapers
+```
+
+### Data extraction
+
+To extract data from the Europeana website, you need to specify two arguments:
+* `--story_id`: the Europeana identifier for the Pellet corpus (`"121795"`)
+* `--output_dir`: the directory in which the corpus will be extracted (`"pellet_corpus"`)
+
+Running the following command will start the import:
+```sh
+scrapers eu-trans --story_id 121795 --output_dir pellet_corpus/
+```
+
+{% warning() %}
+The command should take 2 hours to run completely, depending on your network connection and Europeana current availability.
+{% end %}
+
+Once the extraction is done, you will find a JSON file named `121795.json` in the directory named `pellet_corpus/`.
+
+### Publication to Arkindex
+
+Then, you can use the `scrapers publish` command to publish the data to Arkindex.
+
+You will need to provide the following arguments:
+* `arkindex-api-url`: The Arkindex instance in which you wish to import the corpus. By default, you should use [https://demo.arkindex.org/](https://demo.arkindex.org/).
+* `arkindex-api-token`: Your API token. If you do not know your API token, refer to [this page](@/users/auth/index.md#personal-token).
+* `--corpus-id`: The UUID of the Arkindex project created in the [previous step](#create-a-project-in-arkindex). This value can be copied from your Arkindex project details page, just below its name.
+{{ figure(image="tutorial/arkindex_project_id.jpg", height=200, caption="Find your project's UUID on Arkindex") }}
+* `--worker-run-id`: The worker run UUID that will be used to import the data. Refer to [this page](https://teklia.com/our-solutions/arkindex/releases/arkindex-release-152/) to create your own worker run.
+* `--folder-type`: The type of the top level element (`"folder"`)
+* `--page-type`: The type of the child level elements (`"page"`)
+* `--report`: The path to the JSON report file (`"report.json"`)
+* `folder`: The path to the local directory containing the `121795.json` JSON file, generated using the previous command (`"pellet_corpus/"`)
+
+```sh
+scrapers publish --folder-type folder \
+ --page-type page \
+ --report report.json \
+ --corpus-id aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee \
+ --arkindex-api-url https://demo.arkindex.org/ \
+ --arkindex-api-token aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee \
+ --worker-run-id aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee
+ pellet_corpus/
+```
+
+Once the import is finished, you should be able to navigate through the folder named `PELLET casimir marius` in Arkindex:
+{{ figure(image="tutorial/arkindex_corpus.jpg", height=500, caption="The Pellet corpus in Arkindex") }}
+
+## Data partitioning
+
+To train Machine Learning models on this corpus, you need to split the corpus in three sets for training, validation and evaluation:
+* 321 `page` elements (around 75% of the corpus) in the `train` set (used for model training)
+* 50 `page` elements (around 12.5% of the corpus) in the `val` set (used for model validation)
+* 50 `page` elements (around 12.5% of the corpus) in the `test` set (used for model evaluation)
+
+### Dataset creation
+
+First, you need to create an [Arkindex `Dataset`](@/training/datasets/index.md). To do that, go to your corpus, then click on `Actions` > `Project information` > `Datasets` > `+`. Enter a description of this dataset, and keep the three sets named `train`, `validation` and `test`.
+
+{{ figure(image="tutorial/arkindex_create_dataset.jpg", height=400, caption="Create the dataset") }}
+
+We also recommend that you create three `folders` inside the `PELLET casimir marius` folder, named `train`, `validation`, and `test`. To create a folder, click on `Actions`, then `Add folder` and enter the name of your folder.
+
+{{ figure(image="tutorial/arkindex_create_folders.jpg", height=300, caption="Create three folders") }}
+
+
+### Add pages to a dataset set
+
+To add 50 random pages to a the `test` set of your `Dataset`, follow these steps:
+
+1. Go to the folder named `PELLET casimir marius`
+2. Select 50 random pages
+ * Filter elements by type `page` and sort them by `Random` order
+ {{ figure(image="tutorial/arkindex_filter_sort.jpg", height=300, caption="Filter pages and sort randomly") }}
+ * Click on `Display` > `Pagniation size` and set it to 50
+ {{ figure(image="tutorial/arkindex_pagination_size.jpg", height=300, caption="Update pagination size") }}
+ * Select 50 pages by clicking on `Actions` > `Select all displayed elements`
+ {{ figure(image="tutorial/arkindex_select_all_displayed_elements.jpg", height=300, caption="Select all displayed elements") }}
+3. Add these pages to your `Dataset`
+ * Go to the `Selection` page
+ {{ figure(image="tutorial/arkindex_selection.jpg", height=50, caption="Selection") }}
+ * Click on `Actions` > `Add to a dataset`, then select the `test` set of your `Dataset`
+ {{ figure(image="tutorial/arkindex_dataset_set.jpg", height=150, caption="The Pellet dataset imported in Arkindex") }}
+4. Move these elements to the `test` folder
+{{ figure(image="tutorial/arkindex_move.jpg", height=300, caption="Move elements to the test folder") }}
+
+Repeat these steps for the `validation` set.
+
+Finally, select all the remaining `page` elements and add them to the `train` set and folder.
+
+### Visualize your dataset
+
+Click on the `Dataset` name to visualize its content:
+{{ figure(image="tutorial/arkindex_dataset_viewer.jpg", height=1200, caption="The Pellet dataset imported in Arkindex") }}
+
+## Next steps
+
+As you can see, transcriptions on this corpus are available at page-level.
+
+In order to train Machine Learning models, additional annotations will be required such as:
+* [Page classification](@/tutorial/classification-ground-truth.md)
+* [Text-line detection](@/tutorial/segmentation-ground-truth.md)
+* [Text-line transcription](@/tutorial/transcription-ground-truth.md)
+
+Once the annotation campaigns are completed, you will be able to train Machine learning models:
+* [Classification model](@/tutorial/classification-training.md)
+* [Segmentation model](@/tutorial/segmentation-training.md)
+* [Transcription model](@/tutorial/transcription-training.md)