High-volume TIFF digitization with control-sheet traceability
Route `batch-tiff-ocr` (tiff_to_text.batch) supports high-volume TIFF OCR: digitization projects, daily imaging folders, or journal figure batches. The failure mode is always misaligned filenames, duplicated pages, or mixed default languages across directories. Maintain a control sheet with source path, page index, primary language, owner, and whether handwriting is present. Sample pages that contain numbers, legal clauses, or conclusions—not only visually clean spreads. Bind transcripts back to source IDs before merging into a knowledge base or manuscript.
Batch TIFF OCR collaboration tips
- Enter `batch-tiff-ocr`, upload one cohort at a time, and freeze naming rules plus page-index columns before the first run.
- After each cohort, scan for empty pages, duplicates, or undocumented language switches; rerun smaller slices if needed.
- Before merging, spot-check monetary or regulatory tokens via search, then publish to the shared doc with a frozen revision.
TIFF-to-text FAQ (batch)
Which fields should be standardized first in `batch-tiff-ocr` so transcripts stay traceable?
Standardize source path or hash, page index, primary language, operator, and final-review flag—never archive anonymous text blobs.
How aggressive should sampling be?
100% review for money, legal, or conclusion pages; lighter sampling for covers if rules are documented.
Languages differ per folder—how do we avoid model mismatch?
Process folder-by-folder with frozen language codes; ban undocumented whole-library runs.
Why do duplicate pages appear after merge?
Often page-split offsets or filename collisions—verify with file hashes.
How do we accept vendor OCR deliverables?
Spot-check high-risk rows and require CSV columns for source path and page index.