Label research papers in seconds

Research labeling annotates academic and scientific documents with structured labels that ML models use as training data. Each region of the document is tagged with its semantic role (title, authors, abstract, sections, figures, tables, equations, citations, references) and exported in a format your pipeline can consume directly.

Journal articles, conference papers, preprints, theses and dissertations, literature reviews, and technical reports from any publisher or template.

Labels are typically 92%+ accurate on standard academic layouts. Confidence scores attach to every label so low-confidence fields can be flagged for manual review before training runs.

Structured JSON with bounding boxes, segment text, label classifications, and confidence scores. Markdown is also supported. Both are directly compatible with PyTorch DataLoaders, TensorFlow Datasets, and Hugging Face Transformers.

Yes. Label your first 5 pages without an account. Larger batches and API access are available on paid plans.

Yes. Documents are processed over encrypted connections, never used for model training without explicit consent, and can be deleted immediately after labeling. Enterprise plans include SOC 2 compliance and private deployment options.