Extract Structured Data Fast with Go Document Parser OCR

It’s layout-aware, so it reconstructs the correct reading order across columns, headers/footers, and section breaks. That means your Go pipeline can ingest specs and design docs without brittle post-processing to fix scrambled text.

Tables are extracted as real structured content with rows, columns, and cell boundaries preserved. You can reliably turn price lists, reports, and requirement matrices into clean data structures for downstream services.

Yes—JSON mode includes rich metadata like page numbers, element types, and spatial coordinates for each block. This gives your Go services predictable schemas and the traceability to validate results against the original document.

You call simple REST endpoints: upload a file, poll for completion, then fetch the parsed output. It fits neatly into common Go patterns (workers, queues, and storage pipelines) without adding a new framework.

It’s designed to handle real-world PDFs, including complex formatting and table-heavy reports, while keeping content in the right order. For scanned documents, results depend on source quality—using clearer scans improves accuracy and reduces manual cleanup.

Most teams upload documents asynchronously, poll for status, and stream results into storage or indexing jobs once ready. This approach keeps latency off your request path and makes it easy to scale parsing with your existing Go workers.