Transcribing Images

Transcribing Images

Read what a slide, page, or image actually shows — text plus charts, diagrams, screenshots, and layout — by rasterizing it and sending the picture to a vision model. This is the fix for the gap the built-in pptx and pdf skills leave: they extract embedded text only, so an image slide, a chart, or a scanned figure reads as empty. Visual transcription reads it the way a person looking at the slide would.

When to reach for this vs. the built-in skills

Use the pptx / pdf skills first for text-native documents — a normal deck or report where the content is real text boxes. They are faster and exact.

Switch to this skill when text extraction comes back thin or empty on a file you can see is visually rich, or whenever the meaningful content is a picture: chart, graph, diagram, screenshot, photo, scanned page, or a slide exported as one flat image. Don't guess which case you're in — if pptx/pdf returned little from a file that clearly has content, that is the signal.

The pipeline

Everything routes through scripts/transcribe_pages.py, which handles all three ingress paths and one bad page never aborts the rest:

• .pptx / .ppt → LibreOffice headless → PDF → pdftoppm → one PNG per slide
• .pdf → pdftoppm → one PNG per page
• image file → used directly as a single page

Each page image is then transcribed by a vision model. Run it directly:

python3 scripts/transcribe_pages.py deck.pptx                  # all slides
python3 scripts/transcribe_pages.py report.pdf --pages 3-7     # subset
python3 scripts/transcribe_pages.py slide.png --model opus     # one image
python3 scripts/transcribe_pages.py deck.pptx --json out.json  # structured

Or import transcribe_file(...) for programmatic use; it returns a list of {page, image, text, error} dicts.

Choosing the model

The transcription core and its empirical cost/recall data are reused from browsing-bluesky/scripts/image_transcribe.py — same registry, kept in sync. Pick with --model:

• gemini-lite (default) — cheapest and fastest, ~95% token recall on dense screenshots. Right for routine deck reading.
• gemini-flash — token-perfect, ~3x the cost. Use when exact text matters.
• gemini-3.5-flash — heavier reasoning alongside transcription, ~19x cost. Use when a page needs interpretation, not just reading. Check invoking-gemini's model table for the current frontier Flash before assuming this is still the strongest reasoning tier available.
• opus — for interactive sessions where you want the reading in your own context anyway.
• haiku — only if constrained to single-vendor Anthropic; weak at dense transcription (tends to summarize instead of transcribe).

Default to gemini-lite and escalate only when recall or reasoning demands it.

OCR fallback (tesseract)

Tesseract 5.x is installed (eng + osd language packs only) and is exposed as --engine tesseract. It returns glyphs, not a reading — no chart interpretation, no diagram description, no layout meaning. Use it only for pages you already know are plain scanned text, when you want a zero-cost, fully-offline pass. For anything with a chart, diagram, or visual layout, the vision path is the correct tool; tesseract on those pages will quietly lose the content that mattered.

Interactive shortcut

In an interactive session you can often skip the model call entirely: rasterize with scripts/transcribe_pages.py … --json to get the page PNGs, or just convert and view each page image yourself — Claude reads images natively. The script's vision-model path exists for batch and autonomous runs where no human-in-loop reader is available, or when a deck has more pages than is practical to view one by one.

DPI

Default raster is 150 DPI — legible for a vision model and safely under the 5 MB/image base64 ceiling. Bump to --dpi 200–300 only for pages with dense small fonts; higher DPI risks exceeding the per-image size limit and costs more tokens for no gain on normal slides.