Practical overview
When it fits
Use Doccano when humans need to create or review labeled text data before it is used in machine-learning workflows.
Who it is for
- Machine-learning practitioners creating labeled datasets
- NLP teams preparing data for sentiment analysis, named-entity recognition or summarization
- Organizations that need a self-hosted text-annotation workspace
- Teams coordinating human annotation of text data
Documented uses
- Label text for classification datasets
- Mark spans for sequence-labeling datasets such as named-entity recognition
- Prepare sequence-to-sequence data for tasks such as text summarization
- Coordinate collaborative annotation through a web interface
- Handle annotation-related file uploads and downloads through the documented task process
Requirements & limits
The repository documents self-hosted deployment through pip, Docker or Docker Compose. The pip route requires Python 3.8 or later; no pricing or hosted-service account requirements are documented here.
- Choose the annotation mode that matches the dataset: classification, sequence labeling or sequence-to-sequence.
- Use pip when a Python-based installation is appropriate; use Docker or Docker Compose for container-based deployment.
- SQLite is the default database for the pip installation, while PostgreSQL is supported through an additional dependency and DATABASE_URL configuration.
- The documented feature set includes collaborative annotation and a RESTful API, which may matter for team workflows or integrations.
- A Docker data volume is used in the documented container setup to persist data across restarts.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up Doccano with pip
Install Doccano, initialize its database, create an administrator and start the documented web services.
Before you start
- Python 3.8 or later
- pip
- A second terminal for the task process
Steps
Install Doccano with pip.
Initialize the database with the documented initialization command.
Create a superuser with the documented user-creation command.
Start the web server and task process, then open the documented local address.
Expected outcome
A locally running Doccano web server with an administrator account and a task process for file upload and download handling.
Guide limitations
- The pip setup uses SQLite by default.
- PostgreSQL requires the additional PostgreSQL dependency and a configured DATABASE_URL.
- This recipe documents setup and startup only; it does not describe project creation or annotation actions.
Set up Doccano with Docker
Create and start a Docker container for a local Doccano instance with persistent application data.
Before you start
- Docker
- A container name and administrator credentials
- A Docker volume for /data
Steps
Pull the Doccano Docker image.
Create the container with administrator environment variables, a data volume and port 8000 mapped.
Start the container.
Open the documented local address.
Expected outcome
A locally accessible Doccano container with its application data mounted to a Docker volume.
Guide limitations
- The instructions describe a local container setup rather than annotation workflow steps.
- The Docker instructions use the published Doccano image and expose the service on port 8000.
- The nightly image is presented as an option for latest features, without a feature list.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- doccano/doccano · Official homepage title · retained in its source language