Traditional OCR converts scanned text into raw characters. Document AI goes further: it classifies documents, extracts structured fields, validates confidence, and routes the data into business systems.

Yes. GEM OCR is independently trainable, so it can be retrained on your specific invoices, forms, contracts, or IDs rather than relying on a generic pretrained model.

Yes. GEM OCR supports on-premise and private cloud deployment, so sensitive documents can stay entirely within your own environment.

Low-confidence extractions can be routed to a human review queue instead of being passed downstream automatically, keeping accuracy high in production.

GEM OCR processes invoices, receipts, forms, tables, and contracts across formats such as TIFF, JPEG, PNG, and PDF, in languages including English, Japanese, Korean, and Vietnamese.

GEM OCR combines trainable optical character recognition with layout-aware document understanding models — the class of technology behind research such as Microsoft’s LayoutLM — retrained specifically on your document types, rather than relying on a generic, one-size-fits-all engine such as open-source Tesseract OCR. This typically improves accuracy on documents with varied or non-standard layouts

On-premise and private cloud deployment options help support data residency and privacy requirements, including regulations such as the General Data Protection Regulation (GDPR), depending on your industry and jurisdiction.

GEM defines enterprise KPIs during the discovery phase, such as processing time, manual entry cost, and extraction accuracy, and measures results against a pre-agreed business case.

New document types are added incrementally, building on the models, review workflows, and governance already validated in the pilot.