| name | transcribing-images |
| description | Reads the visual content of slides, pages, and images the way a human would, not just their embedded text. Use when a PPTX or PDF has image slides, screenshots, charts, scanned figures, or flattened-to-image layouts that the built-in pptx/pdf skills read as empty; when asked to transcribe, describe, OCR, or extract what is shown in an image, slide deck, or document page; or when embedded-text extraction returned little or nothing from a visually rich file. Triggers on 'read this deck', 'what's on these slides', 'transcribe', 'OCR', 'extract text from image', 'describe this chart/diagram', .pptx/.pdf/.png/.jpg with visual content. |
| metadata | {"version":"0.1.0"} |
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
python3 scripts/transcribe_pages.py report.pdf --pages 3-7
python3 scripts/transcribe_pages.py slide.png --model opus
python3 scripts/transcribe_pages.py deck.pptx --json out.json
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.
