OCR: Turning Your Documents Into Data
Turning documents and images into usable information: extraction, classification, automation — document AI serving your processes.
Documents full of information, but unusable
Invoices, contracts, forms, letters: received documents contain essential information — amounts, dates, identities, conditions. But as long as they remain files or paper, this information:
- cannot be searched;
- must be manually entered into software;
- triggers no automatic processing;
- is subject to transcription errors.
Every manually processed document is one more task for the team.
What is OCR, simply
OCR (optical character recognition) transforms the information in a scanned image or document into usable text.
Once the text is available, automatic processing can:
- extract information: an invoice amount, a contract date, a sender's identity;
- classify a document: invoice, contract, letter, form;
- search within it: find the document mentioning a specific clause;
- feed a workflow: the received document triggers a sequence of steps;
- transmit data to another application: accounting, ERP, document management.
OCR is the first step: document AI does the rest (extraction, classification, control).
Complete document processing chains
Technea sets up document processing:
- OCR of scanned documents, PDFs and images;
- data extraction: structured fields from varied documents;
- automatic classification: sorting documents by type and content;
- invoice processing: reading, extraction, reconciliation;
- integration with existing software: extracted data arrives where it serves.
The Lab's work on OCR and document extraction documents these technical choices.
Concrete examples
- PDF data extraction: turning document collections into usable data.
- Document classification: automatically sorting incoming documents.
- Invoice automation: from received document to accounting data, without entry.
- Mail handling: automatic reading, routing, archiving.
How document processing is set up
1. Document analysis: types, formats, variability, volume. 2. Field definition: which information to extract, with which rules. 3. Tuning: OCR and extraction on a real sample. 4. Quality control: reliability measurement, human circuit for doubtful cases. 5. Integration: extracted data feeds your tools.
What it changes
- no more manual entry: information arrives on its own;
- searchable documents: PDF content becomes queryable;
- fewer errors: extraction is homogeneous and controlled;
- triggered processing: a received document launches the next steps;
- complete traceability: every processed document is logged.
Frequently asked questions
Related services
Documents to turn into data?
Show us a sample: we assess feasibility on your real documents.