Comment by Legend2440
1 day ago
No matter how much you tinker and debug, classical methods can’t match the accuracy of deep learning. They are brittle and require extensive hand-tuning.
What good is being able to understand a system if this understanding doesn’t improve performance anyway?
I agree, Deep Learning OCR often outperforms traditional methods.
But as engineers, it’s essential to understand and maintain the systems we build. If everything is a black box, how can we control it? Without understanding, we risk becoming dependent on systems we can’t troubleshoot or improve. Don’t you think it’s important for engineers to maintain control and not rely entirely on something they don’t fully understand?
That said, there are scenarios where using a black-box system is justifiable, such as in non-critical applications where performance outweighs the need for complete control. However, for critical applications, black-box systems may not be suitable due to the risks involved. Ultimately, what is "responsible" depends on the potential consequences of a system failure.
This is a classic trade-off and the decision should be made based on the business and technical context that the solution exists within.