ocr-quality-assessment

ocr quality assessment

7,385 stars

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

$curl -o ~/.claude/skills/ocr-quality-assessment/SKILL.md --create-dirs "https://raw.githubusercontent.com/kreuzberg-dev/kreuzberg/main/.ai-rulez/domains/ocr-integration/skills/ocr-quality-assessment/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/ocr-quality-assessment/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How ocr-quality-assessment Compares

Feature / Agentocr-quality-assessmentStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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.

Related Guides

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