OCRing PDFs

SkillSearch

Lets your agent ocr pdf scans by adding a searchable text layer so the file can be searched, grepped, and reused.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the OCRing PDFs skill

About this capability

Adds a searchable text layer to a scanned PDF with ocrmypdf. Installs the toolchain at runtime in ~18s. Use when a PDF's pages are images and the deliverable is a file to keep, grep, or hand to another tool: 'make this PDF searchable', 'OCR this scan', 'I can't select the text in this PDF', 'search

What this skill tells your AI

The instructions your AI receives, as published by oaustegard/claude-skills in ocring-pdfs/SKILL.md and read by ahel’s review.

ocrmypdf writes an invisible text layer over the original page images, so the output file is both the scan you can look at and a document pdftotext, grep, and pdfplumber can read. Rasterize-then-tesseract gives you a .txt divorced from the pages; page numbers and coordinates are gone.

The toolchain is not in the base container. It installs in 18 seconds (measured 2026-09-12: apt 3s, pip 15s), so install it when a scan shows up rather than carrying it in a container layer.

Probe before installing

pdftotext in.pdf - | tr -d '\f \n' | wc -c

Nonzero means the PDF already has a text layer and is not a scan. Extract with pdftotext or pdfplumber and stop. Running OCR on it wastes a minute, and with --force-ocr it replaces exact embedded text with a lossy reading of a raster of itself.

A small nonzero count (tens of characters across many pages) is the mixed case: a born-digital cover page in front of scanned body pages, or a scan whose producer stamped a header. --skip-text handles it.

Install

sh scripts/ensure_ocr.sh              # English
sh scripts/ensure_ocr.sh nor deu      # plus Norwegian and German

Idempotent: 0.8s when everything is already present, 2.6s to add one more language pack. Installs ghostscript, pngquant, poppler-utils, tesseract and its language packs via apt, then ocrmypdf via pip.

Run

ocrmypdf --skip-text --deskew --rotate-pages --output-type pdf in.pdf out.pdf
pdftotext out.pdf - | wc -w        # verify: zero words means it failed quietly

About 2s per page for a single dense page at 200 DPI on one core. A 300-page scan is therefore a background job, not a single bash call — launch it detached with a sentinel file per the external-call pattern in bash-tool-timeout.

Which text-layer mode

flaguse it when
--skip-textDefault. Pages that already carry text are passed through untouched; image-only pages get OCR. The safe choice for anything mixed.
--force-ocrEvery page is rasterized and re-OCRed, discarding any existing text. Correct for a scan carrying a junk text layer, and for pages with text-over-image that --skip-text would skip. Destroys real embedded text, so probe first.
--redo-ocrReplaces a previous OCR layer while leaving born-digital text alone. Narrower than --force-ocr and slower to fail on odd inputs.

--output-type pdf skips PDF/A conversion. Drop it when the output is going into an archive that requires PDF/A; ghostscript does the conversion either way.

Languages

-l eng+nor for a mixed-language document, -l nor for a monolingual one. Order does not matter. Every code needs its tesseract-ocr-<code> pack installed. Pass the codes to ensure_ocr.sh and it handles them. Accuracy drops noticeably when the language is wrong, and tesseract will not tell you; it returns confident garbage instead.

Container facts (measured 2026-09-12)

  • apt-get update exits 100 here. A preconfigured nodesource repo is off the egress allowlist and returns 403, and the nonzero exit aborts any && chain behind it. The Ubuntu mirrors are reachable without an update. Run apt-get install directly.
  • unpaper is absent, so --clean and --clean-final fail. Don't pass them.
  • One core, so --jobs buys nothing on claude.ai. CCotw has four.
  • ocrmypdf --version prints to stderr. Capture with 2>&1 or a version check reads as empty.
  • jbig2 is absent; output uses CCITT/JPEG instead, which costs some file size and nothing else.

When to use transcribing-images instead

This skill produces glyphs. It does not read a chart, describe a diagram, or recover handwriting. Tesseract on those pages returns nothing useful and gives no sign that it lost anything.

Route to transcribing-images when the meaningful content is a picture, or when the deliverable is a reading rather than a file. Both is a normal answer: OCR the document so it is greppable, then send the pages that carry figures to a vision model.

In an interactive session, native vision beats both for a handful of pages: rasterize with pdftoppm -r 200 -png and view the images. Reach for OCR when the document is longer than context will hold, or when the text has to outlive the conversation as a file.

Signals

GitHub stars
150
Forks
6
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
ocring-pdfs
Source
github.com/oaustegard/claude-skills