How-to read OCR with DENKnet for industrial applications

How-to | AI: cameras & software

Learn how to implement OCR (optical character recognition) using DENKnet for industrial vision tasks. This video explains the basics of OCR, why it matters for quality control and automation, and how to achieve accurate results even with challenging markings such as inked, scribed, or casted text.

You’ll see practical examples of reading printed and direct-marked text, explore options for fixed and variable layouts, and learn how to train and deploy OCR networks using the AI Hub. We also demonstrate integration into Python for real-world applications like expiry date detection. Whether you need fast, reliable OCR for production lines or complex surfaces, this guide provides actionable insights.

Topics covered

  • What OCR is and why it matters in industrial vision
  • Handling fixed and variable text positions with AI
  • Training OCR networks for challenging cases
  • Using AI Hub for project setup and labelling
  • Python integration for automated OCR workflows

Video timeline

  • 00:00 - Introduction
  • 03:00 - What is OCR?
  • 03:57 - Text spotting + reading
  • 04:30 - Re-train the OCR (fine tuning)
  • 06:28 - Integrate/use trained OCR model
  • 07:09 - Hands-on: DENK Vision AI Hub
  • 19:50 - Hands-on: Python sample with DENK API
  • 21:38 - Final thoughts

Who should watch

Ideal for vision engineers, automation specialists, and system integrators looking to implement OCR for industrial inspection, quality control, and traceability.

Watch now to master OCR with DENKnet and start building smarter vision solutions.

Further links and downloads

Download the Python source code sample (zip, 28,9 KiB)

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Speaker Daniel Routschka
Daniel Routschka

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