PDF Annotation & Transcription Experts - Gujarati

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 Source documents: find a publicly accessible Gujarati 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 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 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 Gujarati documents rather than synthetic or templated pages Quali