v0.1.0 to v0.1.1

---

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:

---

# 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:

```bash

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.

- `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.