This is a really insightful topic because many people assume OCR performance is only about text recognition accuracy, while the real challenges often come from document structure, formatting inconsistencies, handwriting quality, and data validation workflows. Even a highly accurate OCR system can fail if the extracted information isn’t organized or interpreted correctly for real-world use cases. It reminds me of how tools like Easygradecalculator focus not just on calculations, but also on making academic data easier to understand and manage efficiently. Do you think future OCR systems will rely more on AI context understanding rather than simple character recognition?
.
