17 set - Milano
Obsidian
Fluent Language Skills Required
Malayalam. Native fluency in Malayalam, including full command of Malayalam script and orthography, is required for this position. All annotation and transcription work is performed in Malayalam.
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: Malayalam, alongside four other Indic scripts, Japanese and Korean. 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 Malayalam 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 Malayalam, 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 Malayalam script, including handwritten content, flagging any region where the source is not legible
- Capture page metadata: language, document type, source, page dimensions, and flags for tables, formulas and handwriting
- Review a colleague's work: every task is reviewed end to end by a second Malayalam expert, and experienced annotators take on that review
Who You Are
- You are a native Malayalam speaker with full command of the script, its diacritics and its conjunct forms
- You have worked with documents: annotation, transcription, translation, localization, subtitling, proofreading, journalism, or regional-language data review
- You are exact: character-level accuracy matters more here than speed, and a single wrong diacritic is a defect
- You are systematic:
you apply a taxonomy consistently across hundreds of pages rather than improvising per document
- You are comfortable with unfamiliar layouts: multi-column newspapers, exam papers, handwritten forms
Nice-to-Have Specialties
- Regional-language AI data: annotation, labeling, grading, or bilingual evaluation for training datasets
- Transcription and localization: MTPE, subtitling, bilingual QA, OCR correction or post-editing
- Document production: typesetting, copy-editing, xysqume proofreading, or digitization of Malayalam-language material
- Script and encoding: Unicode normalization, Malayalam input methods, numeral-form and character-form accuracy
What Success Looks Like
- Every meaningful region on the page is captured, correctly bounded and correctly typed
- Reading order reflects how the page is actually read, including across columns
- Transcriptions match the source character for character, in Malayalam script rather than transliteration
- Your tasks pass second-expert review the first time
- Unsuitable pages are flagged up front rather than after thirty minutes of work
Why Join Mercor
- Build the training data that makes document AI work in scripts it currently handles badly
- Work from real published Malayalam documents rather than synthetic or templated pages
- Quality leads on this project: accuracy is the first measure, with handling time tracked alongside it
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