When PDF folders snowball overnight
NLP curators, finance hubs, and CX archives all ingest PDF avalanches—hundreds of filings or ticket attachments that must become text without babysitting each icon. Silent outliers hurt most: leftover encryption, corrupted xref tables, or obese font subsets quietly yield empty dumps while dashboards smile green. Saturating parallel tabs invites browser reclaimers that resemble random failures. Ai2Done couples batching guidance with explicit progress so operators chunk risky cohorts and annotate failure codes honestly. Post-run, chart byte histograms or line counts to catch phantom empties; ML pipelines still demand stratified human reads—not blissful averages of confidence scores. Classified corpora remain beholden to least-privilege exports even when processing stays local.
Batch text extraction in three steps
- Inventory PDFs; flag encrypted, oversized, or scan-heavy outliers.
- Extract in logged batches instead of unlimited parallelism.
- Automate QA metrics plus stratified sampling before lake ingestion.