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| # Label Studio Annotation Guide
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| This guide describes how to use Label Studio to create annotated datasets for training Object Detection models used by LogicalDOC.
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| ## Installing Label Studio
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| Refer to the official Label Studio installation documentation.
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| ## Starting Label Studio
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| To enable the use of local files, the following environment variables must be configured:
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| * LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED
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| * LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT
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| Example:
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| ```bash
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| export LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
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| export LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/path/to/documents
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| label-studio start
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| ```
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| ## Creating a Project
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| 1. Create a new project.
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| 2. Configure the labeling interface.
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| 3. Define the labels that will be used during annotation.
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| ## Importing Data
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| Label Studio supports multiple import methods.
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| For large projects, importing media files directly through the web interface is not recommended. Instead, use local storage references.
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| When importing document images, select **Files** as the import method.
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| Unlike CVAT, Label Studio creates one task for each imported document.
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| ## Annotating Documents
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| 1. Open a task.
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| 2. Select the desired label.
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| 3. Draw a bounding box around the target document element.
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| 4. Save the annotation.
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| Typical labels may include:
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| * Invoice Number
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| * Date
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| * Seller Name
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| * Buyer Name
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| * Total Amount
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| ## Annotation Example
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| [Insert screenshots here]
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| ## Exporting the Dataset
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| After the annotation process is completed, export the project dataset.
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| Label Studio supports multiple export formats, including:
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| * YOLO
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| * COCO
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| * Pascal VOC
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| * CSV
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| For YOLO training, the YOLO export format is recommended.
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| ## Dataset Formats
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| ### COCO Format
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| COCO is a JSON-based dataset format commonly used for object detection datasets.
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| Reference:
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| https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html
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| ### YOLO Format
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| YOLO datasets consist of images and text annotation files organized according to a predefined directory structure.
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| Reference:
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| https://docs.cvat.ai/docs/dataset_management/formats/format-yolo/
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| ### YOLOv8 OBB
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| YOLOv8 OBB (Oriented Bounding Boxes) extends the standard YOLO format by supporting rotated bounding boxes through eight normalized coordinates.
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| This format is useful when document elements are not aligned horizontally.
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