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Created page with "# Label Studio Annotation Guide This guide describes how to use Label Studio to create annotated datasets for training Object Detection models used by LogicalDOC. ## Installing Label Studio Refer to the official Label Studio installation documentation. ## Starting Label Studio To enable the use of local files, the following environment variables must be configured: * LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED * LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT Example: ```bas..."
 
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# Label Studio Annotation Guide
= Preparing a Dataset with Label Studio =


This guide describes how to use Label Studio to create annotated datasets for training Object Detection models used by LogicalDOC.
This guide explains how to create an annotated dataset for YOLO training using Label Studio.


## Installing Label Studio
=== Install Label Studio ===


Refer to the official Label Studio installation documentation.
Refer to the official installation guide:
https://labelstud.io/guide/install


## Starting Label Studio
=== Enable Local File Storage ===


To enable the use of local files, the following environment variables must be configured:
For large projects it is not recommended to upload images directly through the Label Studio interface.


* LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED
To enable local file access, configure the following environment variables:
* LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT


Example:
<pre>
LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/path/to/images
</pre>


```bash
Launch Label Studio:
export LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
export LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/path/to/documents


<pre>
label-studio start
label-studio start
```
</pre>


## Creating a Project
=== Create a Project ===


1. Create a new project.
# Login to Label Studio
2. Configure the labeling interface.
# Click '''Create Project'''
3. Define the labels that will be used during annotation.
# Enter a project name
# Configure the labeling interface
# Save the project


## Importing Data
=== Import Images ===


Label Studio supports multiple import methods.
# Open the project
# Click '''Import'''
# Select '''Local Storage'''


For large projects, importing media files directly through the web interface is not recommended. Instead, use local storage references.
When importing images, choose '''Files''' as the import method.


When importing document images, select **Files** as the import method.
Unlike CVAT, Label Studio creates one task for each imported document image.


Unlike CVAT, Label Studio creates one task for each imported document.
[[File:LabelStudio-import-method.png|thumb|600px|center|Selecting the Files import method]]


## Annotating Documents
=== Annotate Documents ===


1. Open a task.
# Open a task
2. Select the desired label.
# Select a label
3. Draw a bounding box around the target document element.
# Draw a bounding box around the target area
4. Save the annotation.
# Save the annotation


Typical labels may include:
Example labels:


* Invoice Number
* Invoice Number
Line 54: Line 60:
* Total Amount
* Total Amount


## Annotation Example
[[File:LabelStudio-annotation-example.png|thumb|600px|center|Example annotation]]


[Insert screenshots here]
=== Export the Dataset ===


## Exporting the Dataset
# Open the project
# Click '''Export'''
# Select the desired format


After the annotation process is completed, export the project dataset.
Supported formats include:
 
Label Studio supports multiple export formats, including:


* YOLO
* YOLO
Line 69: Line 75:
* CSV
* CSV


For YOLO training, the YOLO export format is recommended.
For YOLO training, export the dataset in YOLO format.


## Dataset Formats
=== Dataset Formats ===


### COCO Format
==== COCO ====


COCO is a JSON-based dataset format commonly used for object detection datasets.
COCO is a JSON-based dataset format commonly used for object detection datasets.


Reference:
More information:
https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html
https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html


### YOLO Format
==== YOLO ====


YOLO datasets consist of images and text annotation files organized according to a predefined directory structure.
YOLO datasets contain images and annotation files organized according to a predefined directory structure.


Reference:
More information:
https://docs.cvat.ai/docs/dataset_management/formats/format-yolo/
https://docs.cvat.ai/docs/dataset_management/formats/format-yolo/


### YOLOv8 OBB
==== YOLOv8 OBB ====
 
YOLOv8 OBB (Oriented Bounding Boxes) extends the standard YOLO format by supporting rotated bounding boxes through eight normalized coordinates.


This format is useful when document elements are not aligned horizontally.
YOLOv8 OBB (Oriented Bounding Boxes) extends the standard YOLO format by supporting rotated bounding boxes using eight normalized coordinates.

Revision as of 12:23, 23 June 2026

Preparing a Dataset with Label Studio

This guide explains how to create an annotated dataset for YOLO training using Label Studio.

Install Label Studio

Refer to the official installation guide: https://labelstud.io/guide/install

Enable Local File Storage

For large projects it is not recommended to upload images directly through the Label Studio interface.

To enable local file access, configure the following environment variables: 

LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/path/to/images

Launch Label Studio: 

label-studio start

Create a Project

  1. Login to Label Studio
  2. Click Create Project
  3. Enter a project name
  4. Configure the labeling interface
  5. Save the project

Import Images

  1. Open the project
  2. Click Import
  3. Select Local Storage

When importing images, choose Files as the import method.

Unlike CVAT, Label Studio creates one task for each imported document image.

File:LabelStudio-import-method.png
Selecting the Files import method

Annotate Documents

  1. Open a task
  2. Select a label
  3. Draw a bounding box around the target area
  4. Save the annotation

Example labels:

  • Invoice Number
  • Date
  • Seller Name
  • Buyer Name
  • Total Amount
File:LabelStudio-annotation-example.png
Example annotation

Export the Dataset

  1. Open the project
  2. Click Export
  3. Select the desired format

Supported formats include:

  • YOLO
  • COCO
  • Pascal VOC
  • CSV

For YOLO training, export the dataset in YOLO format.

Dataset Formats

COCO

COCO is a JSON-based dataset format commonly used for object detection datasets.

More information: https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html

YOLO

YOLO datasets contain images and annotation files organized according to a predefined directory structure.

More information: https://docs.cvat.ai/docs/dataset_management/formats/format-yolo/

YOLOv8 OBB

YOLOv8 OBB (Oriented Bounding Boxes) extends the standard YOLO format by supporting rotated bounding boxes using eight normalized coordinates.

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