Doccano is a text annotation platforms for building machine learning training datasets. The web-based interface supports text classification for sentiment analysis and document categorization, sequence labeling for named entity recognition with overlapping entity support and relation extraction between labeled spans, and sequence-to-sequence annotation for text summarization and machine translation pairs. Collaborative annotation enables multiple annotators to work on the same project simultaneously with per-user progress tracking, annotation guidelines, example assignment to specific members, and filtering by assignee. Auto-labeling integrates with external machine learning model APIs through configurable request and response mapping templates, allowing pre-annotation that annotators can review and correct. Data import accepts plain text, JSONL, CoNLL, and Excel formats, while export produces JSONL and CoNLL datasets compatible with spaCy, Hugging Face Transformers, PaddleNLP, and other training frameworks through the doccano-transformer library. The Django backend with Django REST Framework exposes a complete RESTful API for programmatic project creation, dataset management, and annotation retrieval via the official doccano-client Python library. Celery handles background tasks including dataset import and export processing with Flower providing task monitoring. One-click deployment supports AWS CloudFormation and Heroku alongside Docker Compose for self-hosted environments. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.