Doccano

Overview

Doccano brings project setup, dataset import, team annotation, and export into one workflow. Its documented text tasks include classification, sequence labeling, and sequence-to-sequence annotation, with applications such as sentiment analysis, named entity recognition, and text summarization. It also lists image and audio/video annotation, model-assisted labeling, and review workflow capabilities.

The project dates to 2018 and is open source. Rather than locking teams into one deployment route, it supports installations on Linux, Windows, and macOS, along with cloud deployment. That flexibility suits practitioners who can make their own infrastructure choices; it may be less appealing to those seeking a managed service with a narrowly defined setup.

For other tools in the category, see Data Labeling Software.

Key features

• End-to-end labeling workflow: Configure a project, import data, add users, annotate records, and export the resulting dataset. This covers the core path from source material to labeled output without requiring separate tools for each step.
• Multiple annotation formats: Text classification, sequence labeling, and sequence-to-sequence annotation serve different text-labeling needs. Image and audio/video annotation are also listed, giving teams broader modality coverage.
• Collaboration and review: Multiple people can work on annotation, and a review workflow is included. These capabilities make Doccano more suitable for shared projects than a strictly individual labeling process.
• Script integration and model assistance: A REST API supports integration with scripts, including labeling data with machine-learning models. The auto-labeling guide demonstrates Amazon Comprehend sentiment analysis and custom REST API configuration, which can help teams incorporate existing services.
• Deployment and storage choices: Install options include pip, Docker, Docker Compose, source, and cloud deployment. Imported datasets can be stored with Amazon S3 or Google Cloud Storage; database options include SQLite 3 by default, PostgreSQL, and MySQL.
• Interface and login options: The project lists multi-language support, mobile support, emoji support, and a dark theme. OAuth guidance covers GitHub and Active Directory social login and provides Okta setup instructions.

The principal operational caution is SQLite: the installation guide warns that upgrading while using SQLite 3 can result in database loss. Teams using the default should account for that risk rather than treating upgrades as routine; PostgreSQL is an alternative described by the guide.

Pricing

Doccano is free: its plan costs 0.00 USD per free and is open-source. The plan includes an annotation tool installable with pip, Docker, or Docker Compose, so teams can use the software without a subscription price. Its trade-off is that the documented deployment options leave teams to choose and manage their own environment and storage rather than relying on a single prescribed setup.

Platforms

Doccano supports API, Linux, macOS, self-hosted, web, and Windows. The installation guide specifies Python 3.8 or later for Linux, Windows, and macOS machines. Teams can install through pip, Docker, or Docker Compose, build from source, or deploy in the cloud; the frontend is a JavaScript web app built with Vue.js and Nuxt.js.

Who it's for

Doccano is a strong fit for machine-learning practitioners and teams preparing labeled datasets who want collaborative annotation, review, and script integration without paying for the software. It is particularly suitable when teams value control over deployment and storage options. Those who prefer not to manage installation or database decisions, or who use SQLite and cannot plan carefully for upgrades, should consider another tool.

Pros and cons

• Pro — No subscription cost: The open-source plan is free, making it suitable for teams that need annotation software without a paid plan.
• Pro — Flexible deployment and storage: Installation routes span local systems, containers, source, and cloud, while S3 and Google Cloud Storage are supported for imported datasets.
• Pro — Shared, scriptable workflows: Collaboration, review, and a REST API support team labeling and integration with scripts or model-assisted processes.
• Con — SQLite upgrade risk: Upgrading with the default SQLite 3 database can lose the database, so users need to plan around upgrades or choose another database option.
• Con — Setup choices require ownership: The variety of installation and database routes gives teams control but also asks them to make and maintain those choices themselves.

Alternatives

• Label Studio is worth considering for readers who want a freemium alternative with a free plan and free trial, across API, Linux, macOS, self-hosted, web, and Windows.
• LightlyStudio may suit readers seeking another free option with a free trial and an open-source version distributed under the Apache License 2.0.
• Potato is an alternative for users who want a free, self-hosted offering with all features included and no usage limits.
• Argilla is another free, open-source option, deployable on Hugging Face Spaces or a user's own infrastructure.
• Roboflow may be a better fit for readers seeking a freemium option with Android and iOS support and a free tier that includes 10 credits monthly.
• CVAT offers a free Community plan for personal use and small teams, as well as a separate free online plan with limits of one member, one project, three tasks, and 1 GB.
• Datasaur is an option for readers who want a free tier capped at one user, 5,000 labels per year, and 100 MB of storage, plus a Growth trial of up to 14 days.
• Labelme is another alternative for readers looking for a tool available on Windows, macOS, and Linux.

Verdict

Choose Doccano if you want free, open-source labeling software with team collaboration, a REST API, and control over installation and storage. Its breadth makes it a practical choice for practitioners building their own annotation workflow. Look elsewhere if you need a more managed setup or cannot accommodate the database caution around SQLite upgrades.