Odia Document Annotation Specialist
Pubblicato il 12-09-2026 - Obsidian in Lazio
Fluent Language Skills Required:
Odiya.
Native fluency in Odiya (Oriya), including full command of Odiya script and orthography, is required for this position.
All annotation and transcription work is performed in Odiya.
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: Odiya, 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 Odiya 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
Source documents: find a publicly accessible Odiya PDF in an assigned document type, containing at least one multimodal element (images, tables, diagrams, or handwriting), and record where you obtained it
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 Odiya 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 Odiya expert, and experienced annotators take on that review
Who You Are
You are a native Odiya 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, proofreading, or digitization of Odiya-language material
Script and encoding: Unicode normalization, Odiya 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 Odiya script rather than transliteration
Your tasks pass second-expert review the first time
The documents you bring in add layout diversity rather than repeating templates already in the corpus
Why Join Mercor
Build the training data that makes document AI work in scripts it currently handles badly
Work from real published Odiya 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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