Document parsing and OCR accuracy

Document prep choices like font and layout matter far more than the OCR engine itself.

Enterprise OCR must handle bidirectional text and mixed scripts in the extraction layer itself.

Sequence matters: preprocessing steps in the wrong order leave OCR stuck between broken and working.

Document quality matters more than model selection for improving real-world handwriting recognition.

Vendor benchmarks measure clean text, not the dense PDFs that break OCR in practice.

Language models catch OCR errors that rule-based tools miss, but they introduce new failure modes.

Confidence scores route OCR extractions, but only if thresholds match the document type and risk.