DeepTagger
From Documents to Structured Data with Interactive Labelling
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DeepTagger is a no-code platform for turning documents into structured data by example. Instead of writing extraction rules, a user highlights the fields that matter on a few sample documents, and DeepTagger learns from those annotations to pull the same information out of new documents.
It returns structured output and offers API access, so the extraction can be built into a pipeline once the labelling is done.
It is aimed at operations, finance, and research teams who process large volumes of similar documents, invoices, forms, contracts, reports, and want a scalable way to capture specific fields without a developer building a parser for each format.
AI-assisted draft, human-reviewed before publishing — see how we choose & review tools.
Why we picked it
DeepTagger stood out for learning from a few highlighted examples instead of written extraction rules — that by-example approach is worth naming for someone who isn't going to write a parsing rule for every document variant a real-world archive throws at them.
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