Bengali Transcription And Annotation Expert

05 set - Milano
Obsidian

Fluent Language Skills Required: Bengali. Native fluency in Bengali, including full command of Bengali script and orthography, is required for this position. All annotation and transcription work is performed in Bengali.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: Bengali, 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 Bengali 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 DoSource documents: find a publicly accessible Bengali PDF in an assigned document type, containing at least one multimodal element (images, tables, diagrams, or handwriting), and record where you obtained itAnnotate 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 Bengali script, including 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 Bengali expert, and experienced annotators take on that reviewWho You AreYou are a native Bengali speaker with full command of the script, its diacritics and its conjunct formsYou have worked with documents: annotation, transcription, translation, localization, subtitling, proofreading, journalism, or regional-language data reviewYou are exact: character-level accuracy matters more here than speed, and a single wrong diacritic 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 SpecialtiesRegional-language AI 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 Bengali-language materialScript and encoding: Unicode normalization, Bengali input methods, numeral-form and character-form accuracyWhat 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 Bengali script rather than transliterationYour tasks pass second-expert review the first timeThe documents you bring in add layout diversity rather than repeating templates already in the corpusWhy Join MercorBuild the training data that makes document AI work in scripts it currently handles badlyWork from real published Bengali 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

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