v0.15.0

Changed — OCR: tesseract backend by default, split into page tranches

marker (Surya models) is excellent but unusable for OCR on CPU: on a real corpus every big scan (Narmour 499p, Eco 253p, Chomsky…) either timed out at 20 min or OOM-crashed during layout. New default OCR path:

• extract_with_tesseract — renders each page via pypdfium2 (already a dep) and OCRs it with tesseract (fast C binary, no multi-GB ML models → no OOM/timeout). Multilingual (eng, fra, + indic packs). Languages auto-filtered to those actually installed.
• Page-range splitting — a scanned book is OCR'd in tranches of PAGES_PER_OCR_JOB = 50, one P3 job each. A 600-page book becomes ~12 short, independently-resumable jobs instead of one hour-long block that monopolises the worker. enqueue_ocr_jobs() does the split; P1, rtfm doctor --enqueue-ocr and backfill-pages --enqueue-ocr all use it.
• Idempotent append — Library.append_ocr_chunks(book_slug, chunks, page_lo, page_hi) deletes that page range then inserts, so re-running a tranche (retry) never duplicates and other tranches stay intact. Each tranche enqueues P2 embedding for just its new chunks.
• Config: ocr_backend (tesseract default | marker | auto), ocr_langs (default eng+fra; set e.g. eng+fra+tam+hin+san for Indic-script scans).
• New [ocr] extra: pytesseract, pypdfium2, Pillow (+ the system tesseract binary).
• pages_to_chunks() extracted from PDFParser.parse and shared with the OCR handler so OCR'd pages produce identical chunk shapes. 6 new tests (split ranges, idempotent tranche append).

Trade-off: tesseract is excellent on clean print (your scanned books) but weaker than marker on heavy maths/tables/multi-column. For making text searchable it's the right call on a GPU-less machine; marker stays available via ocr_backend: marker.