doccano review
A focused open-source choice for collaborative text annotation and model-assisted labeling.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
doccano is an open-source data-labeling and annotation tool for machine learning practitioners. It is designed for teams working on text classification, sequence labeling, named-entity annotation, and sequence-to-sequence projects. Teams can create projects, define custom labels and annotation guidelines, import datasets, collaborate with project members, annotate examples, and export labeled data. The product also supports image and speech project types through its client API, with web, self-hosted, Android, and iOS platforms listed.
The main strength is its focused annotation workflow. Project members can work together on labeling tasks while using shared guidelines and custom ontologies. Human review is supported, and model-assisted pre-annotation is available through the client CLI. This makes doccano a fit for teams that want a labeling interface connected to scripts or machine learning models without moving away from an open-source deployment model. REST API access adds another path for integrating projects with surrounding data and model workflows.
Its scope is more focused than that of broader general-purpose labeling tools. Text workflows are the clearest fit, while image and audio support is exposed through additional project types in the client API rather than presented as the central product focus. Deployment is self-hosted, and installation can be handled with pip or Docker and Docker Compose. Choose doccano when open-source ownership, collaborative text annotation, custom labeling schemes, and API access are priorities. Teams seeking a wider verified integration ecosystem or a hosted paid-tier model should consider an alternative with those capabilities.
doccano pros and cons
- Where it wins
- Supports classification, sequence labeling, and sequence-to-sequence annotation
- Includes collaboration, custom labels, and annotation guidelines
- Offers dataset import/export, REST APIs, and CLI-based pre-annotation
- Where it doesn't
- Self-hosted deployment requires teams to manage the installation environment
- Its verified integrations are narrower than general-purpose labeling tools
- Its strongest documented workflows center on text annotation
doccano fact sheet, pricing and score →
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