Press PLAY to OCR
It's not about AI, it's about how we use AI to make the difference
Optical character recognition (OCR) is still one of the most difficult disciplines in image processing and machine intelligence today. The pure variety of possible characters and approaches to applying them to different surfaces gives an idea of the range of challenges involved.
And the expectations of OCR are high. People nowadays want it to recognize all characters out-of-the-box and interpret them correctly, just like a human does. Consequently, deep learning-based OCR systems are on the rise. Through varied training with real and synthetic sample data, they learn the necessary scope for interpretation that rule-based approaches lack.
However, the use of state-of-the-art technology is not enough to ensure success - it is much more important to offer it in a way that is easy to use, performant and user-friendly to maintain. This is because even with deep OCR systems, quality, execution speed and user-friendliness are anything but a given.
In our technical article, we examine the crucial factors for selecting a good OCR system that can be implemented quickly and economically, offers reliable and robust reading quality and remains easy to use and maintain, with reference to the DENKnet OCR model.
