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 — frontier reasoning alongside transcription, ~19x cost.

Use when a page needs interpretation, not just reading.

• 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.