AiVibeOptical Character Recognition (OCR)
Optical character recognition (OCR) converts images of printed or handwritten text into machine-readable characters, and is used to read documents, labels, serial numbers, date codes and dot-peen or laser markings on parts.
A modern OCR pipeline detects text regions and then recognises the characters in each region, commonly with deep learning models that read whole text lines rather than isolated characters. Document AI systems add layout analysis to extract fields from forms, invoices and tables, and vision-language models can read and interpret documents directly. Industrial OCR must cope with curved, reflective or low-contrast surfaces. Open-source engines include Tesseract.
In manufacturing and logistics, OCR verifies lot codes, expiry dates and serial numbers for traceability, reads tyre markings and vehicle identification numbers, and checks labels against orders. In offices, it digitises invoices, delivery notes and certificates for automated processing.
Accuracy depends on print quality, font, lighting and resolution; marking processes such as dot peen produce irregular characters that need tuned or trained models. Applications often verify OCR results against expected values or check-digit rules to catch errors. Character and word error rates are standard accuracy metrics.
Key points
- Converts images of text into machine-readable characters
- Pipelines detect text regions, then recognise the characters
- Used for traceability codes, labels and document processing
- Verification against expected values catches read errors
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.