Best use case
ocr-quality-assessment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
ocr quality assessment
Teams using ocr-quality-assessment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ocr-quality-assessment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ocr-quality-assessment Compares
| Feature / Agent | ocr-quality-assessment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
ocr quality assessment
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
Measure and improve OCR output quality 1. Run OCR on test documents 2. Extract confidence scores 3. Calculate accuracy metrics: - Character Error Rate (CER) - Word Error Rate (WER) 4. Analyze confidence distribution 5. Identify low-confidence regions 6. Test preprocessing impact: - Before preprocessing - After preprocessing - Measure improvement 7. Compare OCR backends 8. Profile language-specific quality
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