Korean data and OCR QA - native-language verification

Korean documents, forms and OCR output audited natively rather than guessed at - the failure modes an English-first pipeline cannot see.

Why this is a distinct skill. Korean is my first language, so I catch things a general-purpose extraction pipeline silently gets wrong:

• Spacing around particles and bound nouns, where a wrong space changes the meaning of a field rather than just its formatting
• NFC/NFD normalisation of Hangul jamo, where two visually identical strings compare as unequal and quietly break joins, deduplication and lookups
• Mixed Korean-English strings that split incorrectly under naive tokenisers
• Korean/English bilingual QA, where the two versions of a record have to agree

How it fits the rest of my work. The same verification layer applies: field-level confidence scores, checks against the source document, and a human-review queue for anything uncertain. An OCR pipeline that cannot tell you which fields it is unsure about is not finished.

Document AI code with the trust layer: github.com/jigonyoo/invoice-to-structured