/transcribing-images

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: 0.2.1

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). That finding is from Haiku 4.5; the alias now resolves to `claude-haiku-5-5` and has not been re-measured.

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.

When the deliverable is a file rather than a reading, use `ocring-pdfs` instead. It installs ocrmypdf at runtime (~18s) and writes the OCR back as an invisible text layer over the original page images, so the output is a PDF that `pdftotext` and `grep` can read. The `--engine tesseract` path here returns loose text with no page anchoring.

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.