Korean PDF Annotation Specialist
Pubblicato il 15-10-2026 - Mercor in Milano
Fluent Language Skills Required: Korean. Native fluency in Korean, including full command of Hangul, is required for this position. All annotation and transcription work is performed in Korean. Why This Role Exists Document understanding breaks down fastest in the languages that parsing and vision-language models rarely see. This project builds training data for exactly those languages: Korean, alongside Japanese and five Indic scripts. Each task takes a real, publicly available PDF page and produces a complete structural map of that page, paired with a faithful transcription of every text region in the original script. The dataset deliberately concentrates on the material models handle worst: handwriting, dense multi-column layouts, tables, diagrams, and mixed-script pages. Documents are drawn from newspapers, textbooks, examinations, and everyday formats such as flyers, forms, manuals, menus, brochures, notices and worksheets, so that the corpus reflects the real diversity of Korean documents rather than a narrow band of easily parsed ones.
Delivered work is human-authored throughout. Component identification, component typing, reading order and all transcription are performed by people, not generated by parsing models. What You'll Do * Open and check a task: pages are provided, so you do not source documents yourself. We find the PDFs and upload them for you. Before annotating, confirm the page is in Korean, is legible, has real content, and shows no personal details * Annotate structure: identify and bound every meaningful region of the page - document title, section heading, paragraph, list, table, figure, diagram, caption, formula, question, answer field - and assign each a component type and a reading-order index * Record relationships: link each region to the figure or table it belongs to through a parent component identifier * Transcribe faithfully: reproduce all text exactly as it appears in Hangul, including any hanja and handwritten content, flagging any