Gujarati PDF Annotation Specialist

Fluent Language Skills Required:

Gujarati. Native fluency in Gujarati, including full command of Gujarati script and orthography, is required for this position. All annotation and transcription work is performed in Gujarati. 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: Gujarati, 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 Gujarati 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 Gujarati, 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 Gujarati 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 Gujarati expert, and experienced annotators take on that review

Who You Are

You are a native Gujarati 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 Gujarati-language material

Script and encoding: Unicode normalization, Gujarati 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 Gujarati 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 Gujarati 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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