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Ocr tiered

Skill jacobleft/techang/skills/ocr-tiered

Reusable agent skills for Claude Code, OpenCode, Cursor, and the Vercel skills CLI.

Install
npx -y skills add jacobleft/techang --skill ocr-tiered

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

Copied from the file, not written here

This skill should be used when the user asks to OCR a local image or PDF, extract text from scanned pages, or needs OCR for Chinese or mixed Chinese-English documents. Uses a tiered approach — picks the best available engine based on what's installed and what the user prioritizes (accuracy, balanced, or speed).

SKILL.md

3.5 KB, 880 tokens by cl100k_base, as published. Nobody here has run it

Tiered OCR

Pick the right OCR engine based on what's available and what the user prioritizes.

The three tiers

TierEngineWhen to useTrade-offs
1 — AccuracyPaddleOCRMaximum accuracy, especially for Chinese/mixed CJKRequires Python venv with paddlepaddle + paddleocr (~1 GB)
2 — BalancedTesseract + tessdata_bestGood accuracy without heavy Python depsDownloads best .traineddata on first use; needs tesseract on PATH
3 — FastDefault TesseractLightweight, no extra downloads, fastLower accuracy; uses whatever model ships with the system

Decision guide

  • User wants best quality → Tier 1 (if PaddleOCR venv exists or user is okay installing it)
  • User wants good quality but no Python deps → Tier 2
  • User wants it fast / already has tesseract → Tier 3
  • User is unsure → compare mode on one representative page, then pick the winner for the rest

Use this when

  • User wants OCR on a scanned image or PDF
  • User wants Chinese or mixed Chinese-English OCR
  • User has strong accuracy/speed preferences
  • User doesn't know what OCR tools are available (cascade finds the best one that works)

Boundaries

  • Outputs text files + JSON summaries, not searchable PDFs
  • Does not overwrite source files
  • Tiers 2/3 require tesseract on PATH; PDF input also needs pdftoppm
  • Tier 1 requires a dedicated Python venv (setup below)

Setup (Tier 1 only)

bash scripts/setup_paddleocr_env.sh

Creates .venv-paddleocr/ in this skill directory with paddlepaddle + paddleocr.

Commands

Single tier — when you know which one to use

# Tier 1: best accuracy
.venv-paddleocr/bin/python scripts/ocr_tiered.py tier1 /path/to/page.png

# Tier 2: balanced (no Python venv needed, uses system tesseract)
python3 scripts/ocr_tiered.py tier2 /path/to/page.png

# Tier 3: fast and lightweight
python3 scripts/ocr_tiered.py tier3 /path/to/page.png

Compare — test all tiers on one page, pick the best

.venv-paddleocr/bin/python scripts/ocr_tiered.py compare /path/to/page.png

Cascade — try tier 1 → 2 → 3, stop on first good result

.venv-paddleocr/bin/python scripts/ocr_tiered.py cascade /path/to/file.pdf --pages 100

PDF support

All modes accept PDFs. Use --pages to target specific pages:

.venv-paddleocr/bin/python scripts/ocr_tiered.py tier1 /path/to/file.pdf --pages 1,3-5

Outputs

Written to a sibling directory (<stem>.ocr-tiered/) by default:

  • tier1_paddleocr.txt
  • tier2_tesseract_best.txt
  • tier3_tesseract_default.txt
  • summary.json

Tier 1 config details

PaddleOCR runs with quality-oriented defaults:

  • lang="ch", use_doc_orientation_classify=True, use_doc_unwarping=True
  • use_textline_orientation=True, text_det_limit_type="max", text_det_limit_side_len=1216
  • text_det_thresh=0.3, text_det_box_thresh=0.5, text_det_unclip_ratio=1.5

Tier 2 caches downloaded .traineddata in ~/.cache/ocr-tiered/tessdata_best/.

Tier 3 uses whatever tesseract ships with on the system — no extra downloads.

What ships with it: 2 files

13.9 KB alongside SKILL.md, 2 of them executable

scripts/

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.