Japanese PDF Annotation Specialist

Fluent Language

Skills Required:

Japanese. Native fluency in Japanese, including full command of kanji, hiragana and katakana, is required for this position. All annotation and transcription work is performed in Japanese.

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:
Japanese, alongside Korean and five Indic scripts. 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, vertical text, 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 Japanese 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.

• Source documents: find a publicly accessible Japanese 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, including kanji, hiragana, katakana, furigana and 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 Japanese expert, and experienced annotators take on that review

• You are a native Japanese speaker with full command of kanji, hiragana and katakana, including furigana and variant character forms

• You have professional experience in interpretation, journalism, transcription, translation, editorial work, or comparable document-intensive work

• You are exact: character-level accuracy matters more here than speed, and a single wrong character 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: vertical text, multi-column newspapers, exam papers, handwritten forms

• AI training 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 Japanese-language material

• Script and encoding:
  Unicode normalization, Japanese input methods, full-width and half-width forms, and kanji variant handling

• Every meaningful region on the page is captured, correctly bounded and correctly typed

• Reading order reflects how the page is actually read, including vertical text and multi-column layouts

• Transcriptions match the source character for character, in Japanese script rather than romaji

• 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

• Build the training data that makes document AI work in scripts it currently handles badly

• Work from real published Japanese documents rather than synthetic or templated pages

• Quality leads on this project: accuracy is the first measure, with handling time tracked alongside it