95 lines
4.6 KiB
Markdown
95 lines
4.6 KiB
Markdown
# Proofreading: Manuscript vs Typeset
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## Two workflows
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### A. Bilingual from DOCX (standard)
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When the DOCX manuscript has both Chinese and English in 1:1 paragraph
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correspondence, generate `bilingual.dj` directly from the DOCX:
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1. `pandoc docx → plain text`
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2. Extract Chinese-English pairs from body
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3. Apply fixes: italicize Sanskrit on first occurrence, fix `N.Letter` → `N. Letter` spacing
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4. Write bilingual.dj
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The DOCX English is the authoritative target text. No PDF needed.
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**Extraction approach**: start by adapting `scripts/gen-bilingual-docx.py`.
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For articles where the body has strict CN→EN→CN→EN alternation, the simple
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extraction in that script (CN line, blank, EN line, blank) works directly.
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**Block-based extraction** (for articles with mixed ordering): when the DOCX has
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sections that order CN content before EN content (CN heading → CN body → EN heading →
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EN body), the simple alternation fails. Use block-based extraction instead:
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1. Tag each non-blank line as CN or EN (via `has_cjk()`)
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2. Join page-break split paragraphs: merge consecutive same-language paragraphs
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only when the first is long (>30 chars), doesn't end with CJK/ASCII terminal
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punctuation (`[。！？：）.?!]$`), and isn't heading-like (starts with
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`^[\dIVX]+[\.\s]` and <60 chars)
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3. Group consecutive same-language items into blocks
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4. Walk blocks: for each CN block, pair with the next EN block via `zip()`.
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`min(len(cn), len(en))` handles translator-introduced paragraph splits.
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**Page-break splits in pandoc plain-text output**: the DOCX→plain conversion
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sometimes splits a Chinese paragraph mid-sentence (e.g. `白居` + `易、苏轼…`).
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These appear as two consecutive CN lines separated by a blank. The join heuristic
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above catches these reliably. For EN text, page-break splits are rare; the heading
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detection (`^[\dIVX]+[\.\s]`, <60 chars) prevents false merges of EN headings
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with following EN body paragraphs.
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The DOCX English is the authoritative target text. No PDF needed.
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### B. Bilingual from PDF (when PDF is the typeset target)
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When the PDF English is the typeset "final" version and should be the target:
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1. Extract DOCX Chinese paragraphs (source)
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2. Extract PDF body text via `pdftotext -layout`
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3. Clean PDF: remove slug lines, headers, page numbers, join hyphenation breaks
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4. Match DOCX English paragraphs against PDF body to find positions
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5. Segment PDF body at matched positions
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6. Write bilingual.dj with Chinese source + PDF English target
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**Pitfalls in PDF extraction:**
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- Consecutive hyphenation breaks (e.g. `thou-` + `sand...al-` + `leviate`) — the join
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loop must be recursive: after joining pair N, check if result still ends with `-`
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and join with line N+2
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- Lines with leading whitespace: use `lstrip()` before checking `n[0].islower()`
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- Drop-cap artifacts: `L iving` → `Living`
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- Trailing section numbers: `...viewpoints. 1)` — the ` 1)` is a PDF section marker
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bleeding into the previous paragraph
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### C. Edit suggestions (edit-suggestions.dj)
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After generating bilingual.dj, scan for issues and write `edit-suggestions.dj`:
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**Format**: follow the original document's section/chapter layout. Group suggestions
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under the chapter headings where the issues occur. Use diff-style `-/+` notation.
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**What to flag:**
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- Garbled Chinese text (merged duplicate edits in source DOCX)
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- Repeated words (`the The`)
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- Chapter numbering mismatches (e.g. `九` ↔ `VIII`)
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- Translator notes in headings (`（善鑫翻，妙一审）`)
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- Missing quotes around dialogue/speech
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## Common source DOCX issues
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- Translator notes in Chinese headings: `（某某翻，某某审）` — delete for publication
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- Merged duplicate edits: cut-paste errors where old+new text appear together
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- `N.Letter` without space: `2.How` → `2. How`
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- `the The` double article
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- **Numbering mismatches**: CN and EN headings sometimes disagree (e.g. CN `3．` vs EN `2.`).
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The TOC usually has the correct number — flag the body heading for correction.
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- **Doubled names**: `岳麓书院岳麓书院` — cut-paste artifacts in Chinese body text.
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- **EN paragraph splits without CN counterpart**: translator sometimes renders one CN
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paragraph as two EN paragraphs. The block-based extractor drops the extra EN paragraph
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(as `min(len_cn, len_en)`). Flag in edit-suggestions so it can be manually merged or
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the CN paragraph can be split.
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## Sanskrit italicization
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On first occurrence in body text, wrap with `*term*`. Track seen terms across
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the full body. Terms: bodhisattva, bodhicitta, samsara, Dharma, karma, nirvana,
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Sangha, sutra, Mahayana, Sravaka, Vinaya, Lamrim, Ksitigarbha, Samantabhadra,
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Chan, Arhatship, Theravada.
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