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ek-dev-ocr

Benchmark OCR engines against gold-standard references using CER, WER, and ANLS metrics.

Updated Jun 23, 2026
Search Tags:#ocr#benchmarking#evaluation#quality-assurance#data-extraction#computer-vision
One-click install
npx skills add https://github.com/thorwhalen/ek --skill ek-dev-ocr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ek-dev-ocr
Source: https://github.com/thorwhalen/ek/tree/main/skills/ek-dev-ocr
Command: npx skills add https://github.com/thorwhalen/ek --skill ek-dev-ocr

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ocracy, rapidfuzz, and includes references (resource) components.

What problem does it solve?

This skill provides a robust framework for evaluating the accuracy of OCR engines, helping developers identify the most reliable backends for their specific document processing needs.

Core Features & Use Cases

  • Reference-Based Scoring: Calculate CER, WER, and ANLS metrics against gold-standard corpora to measure engine performance.
  • Reference-Free Estimation: Use signal calibration and validation to estimate quality when gold answers are unavailable.
  • Use Case: When integrating a new OCR backend like Tesseract or Mathpix, use this skill to run a comparative benchmark across a corpus of invoices to determine which engine provides the highest confidence and lowest error rate for your specific document types.

Quick Start

Run the ek-dev-ocr skill to evaluate the ocrmac engine against the gold corpus stored in the local ek data directory.

Frequently Asked Questions about ek-dev-ocr

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark OCR engine accuracy against a gold-standard corpus?▼

To benchmark OCR engine accuracy, compare extracted text and structured data against gold-standard references using CER, WER, and ANLS metrics to measure performance and identify the most reliable backend.

What is reference-free OCR quality estimation and when do I need it?▼

Reference-free OCR quality estimation uses signal calibration and validation to estimate extraction quality when gold-standard answers are unavailable, ensuring reliable document pipelines without manual ground truth.

Does this OCR evaluation approach work with cloud-based vision APIs and Tesseract?▼

Yes, this OCR evaluation approach supports benchmarking across diverse backends including Tesseract, Mathpix, and cloud-based vision APIs while enforcing strict dependency management for reliable comparisons.

How do I compare Tesseract vs Mathpix for my document extraction pipeline?▼

Compare Tesseract vs Mathpix by running a comparative benchmark across your specific document corpus to calculate error rates and determine which engine provides the highest confidence for your data.

What metrics are used for evaluating OCR performance on structured data?▼

Evaluating OCR performance on structured data uses CER, WER, and ANLS metrics calculated against gold-standard corpora to measure character errors, word errors, and structured layout accuracy.

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