Glossary

Optical character recognition (OCR)

What is Optical character recognition (OCR)?

Optical character recognition (OCR) converts text contained in images, such as scans, photos or faxes, into machine-readable text. Only then can documents be searched, copied or processed automatically. Modern OCR uses neural networks and can also recognise tables, layouts and, in some cases, handwriting. Typical uses include invoice processing, archive digitisation and reading forms; quality depends heavily on image quality.

How KI.ordo helps

KI.ordo’s knowledge import only reads PDFs with a text layer, so scanned documents should first be converted into searchable PDFs with OCR software.

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Topics

documents
text recognition
digitisation

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Related terms

Intelligent Document Processing (IDP)

Intelligent document processing (IDP) automatically reads documents such as invoices, contracts or forms, identifies their type and turns the relevant details into structured data. It combines text recognition for scans, natural language processing and validation rules. Organisations save manual data entry and speed up processes such as handling incoming invoices or post. Uncertain fields should be checked by people.

Documents
Data extraction
+1

Template

Scene Text Recognition (STR)

Scene text recognition reads text that appears in photos and videos of real-world surroundings, such as street signs, packaging or number plates. Unlike classic OCR for scanned documents, it has to cope with skewed perspectives, changing light, unusual fonts and cluttered backgrounds. A typical approach works in two stages: first locating text regions, then recognising the characters within them.

Computer vision
Text recognition
+1

Computer vision

Computer vision is a field of AI in which computers interpret images and video. Typical tasks include detecting and locating objects, reading text in images and measuring components. The technology is used, for example, in quality control in manufacturing, in driver assistance systems, in logistics and in medical imaging.

image analysis
deep learning
+1

Unstructured Data

Unstructured data follow no fixed table schema: emails, contracts, manuals, web pages, presentations, images or audio recordings. They make up a large share of organisational knowledge but were long difficult to analyse automatically. Language models can read and search such content and turn it into structured information, for example extracting deadlines and amounts from a contract into a table.

Data
Documents
+1