Local 27B Vision Model Outperforms Apple's Dedicated OCR on Document Structure

A developer testing OCR tools compared Apple's Vision framework (VNRecognizeTextRequest) against a locally running 27-billion-parameter vision-language model (VLM) on a custom test image containing Korean text, a structured table, and a homoglyph-heavy string. While Apple Vision was 300 times faster and made only 2 character errors versus the VLM's 8, it critically failed to preserve table row structure, returning data column by column and making it impossible to match values to the correct entries. The VLM, despite being slower and slightly less accurate on individual characters, correctly maintained reading order and table layout. The author concludes that tool selection must be based on the full task requirement — character recognition versus document structure preservation — not benchmark accuracy alone. A key finding applicable beyond OCR: silent structural failures, like column-shredded output that looks syntactically valid, cannot be caught by simple escalation logic, making cheap-first pipelines risky when information relationships matter.