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 ExistsDocument 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 DoOpen 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 detailsAnnotate 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 indexRecord relationships: link each region to the figure or table it belongs to through a parent component identifierTranscribe faithfully: reproduce all text exactly as it appears in Hangul, including any hanja and handwritten content,



flagging any region where the source is not legibleCapture page metadata: language, document type, source, page dimensions, and flags for tables, formulas and handwritingReview a colleague's work: every task is reviewed end to end by a second Korean expert, and experienced annotators take on that reviewWho You AreYou are a native Korean speaker with full command of Hangul, including hanja where it appears in older or formal documentsYou have worked in bilingual transcription, translation, editorial work, or AI training data, ideally with reviewer experienceYou are exact: character-level accuracy matters more here than speed, and a single wrong jamo is a defectYou are systematic: you apply a taxonomy consistently across hundreds of pages rather than improvising per documentYou are comfortable with unfamiliar layouts: multi-column newspapers, exam papers, handwritten formsNice-to-Have SpecialtiesAI training data: annotation, labeling, grading, or bilingual evaluation for training datasetsTranscription and localization: MTPE, subtitling, bilingual QA, OCR correction or post-editingDocument production: typesetting, copy-editing, proofreading, or digitization of Korean-language materialScript and encoding: Unicode normalization, Korean input methods, Hangul jamo composition, and hanja handlingWhat Success Looks LikeEvery meaningful region on the page is captured, correctly bounded and correctly typedReading order reflects how the page is actually read, including across columnsTranscriptions match the source character for character, in Hangul rather than romanizationYour tasks pass second-expert review the first timeUnsuitable pages are flagged up front rather than after thirty minutes of workWhy Join MercorBuild the training data that makes document AI work in scripts it currently handles badlyWork from real published Korean documents rather than synthetic or templated pagesQuality leads on this project: accuracy is the first measure, with handling time tracked alongside it#J-18808-Ljbffr

📌 Korean Pdf Annotation Specialist (Roma)
🏢 Mercor
📍 Roma