Vision Quality Validation Framework With Claude

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Vision Quality Validation Framework

Let's dive into building a robust quality validation framework! We'll be leveraging the power of Claude vision to ensure top-notch OCR accuracy and image processing quality. This is crucial for maintaining the high standards of our digital documents and publications.

Epic: Quality Assurance & Testing

Our main goal is to create an automated system that can validate various aspects of our document processing pipeline. This includes OCR accuracy, image compression quality, EXIF metadata correctness, and overall EPUB quality. Think of it as a comprehensive QA suite for our digital creations.

Context

We need an automated way to validate a few key things:

  1. OCR Accuracy: We need to ensure that our OCR engines (Groq, DeepSeek, Tesseract) are accurately converting images to text.
  2. Image Compression Quality: It's vital that our image compression methods maintain e-ink readability, so our documents are easy on the eyes.
  3. EXIF Metadata Accuracy: The descriptions associated with our images must accurately reflect their content.
  4. Overall EPUB Quality: Before publishing, we need to ensure the overall quality and integrity of our EPUB files.

Why is this important, guys?

Because manual validation is time-consuming and prone to errors. Automating this process will save us time, improve accuracy, and ensure consistent quality across all our publications. It's like having a tireless QA assistant who never misses a detail.

The Power of Automation

Imagine automatically catching subtle OCR errors that could lead to misinterpretations or ensuring that every image is perfectly optimized for e-readers. This framework will empower us to do just that, making our workflow more efficient and our final products more polished.

Ensuring Readability

One of the critical aspects of this framework is maintaining e-ink readability. We want to make sure that our documents are a pleasure to read, even on devices with limited display capabilities. By rigorously testing compression quality, we can achieve this goal and provide a superior reading experience.

Tasks

Here's a breakdown of the tasks we need to tackle to bring this framework to life:

  • [ ] Create test dataset with ground truth

    • We'll need to assemble a collection of 20 test images, carefully selected to represent a variety of content types. Half of these images will feature handwritten text, while the other half will contain printed text. This mix will allow us to thoroughly evaluate the performance of our OCR engines across different writing styles and fonts.
    • To ensure the accuracy of our validation process, each image will include elements such as charts, equations, diagrams, and tables. These elements will help us assess the ability of the OCR engines to handle complex layouts and non-textual content.
    • A crucial step in creating the test dataset is manually verifying the OCR text for each image. This manually verified text will serve as the ground truth against which we can compare the outputs of the different OCR engines. By having a reliable ground truth, we can accurately measure the accuracy and completeness of each engine.
    • Once the test dataset is complete, we'll store it in the tests/fixtures/validation/ directory. This will ensure that the dataset is easily accessible and can be used for future testing and validation efforts.

  • [ ] Implement Claude vision judge API

    • We'll be harnessing the power of Claude 3.7 Sonnet vision for quality assessment. Claude's advanced vision capabilities will enable us to evaluate the accuracy and completeness of OCR outputs with a high degree of precision.
    • To guide Claude's assessment, we'll use a carefully crafted prompt that instructs it to rate OCR accuracy on a scale of 1 to 10. The prompt will also emphasize the importance of comparing the extracted text to the original image content. This will help Claude to identify any discrepancies or omissions in the OCR output.
    • In addition to a single overall accuracy score, we'll also ask Claude to provide structured scores for completeness and formatting. This will give us a more detailed understanding of the strengths and weaknesses of each OCR engine.
    • The API will be designed to return structured scores, including accuracy, completeness, and formatting. This will make it easy to compare the performance of different OCR engines and track improvements over time.

  • [ ] Compare OCR outputs across engines

    • To get a comprehensive understanding of the performance of different OCR engines, we'll run the same images through Groq, DeepSeek, and Tesseract. This will allow us to directly compare their accuracy, speed, and cost.
    • We'll collect metrics for accuracy, speed, and cost for each engine. This will give us a clear picture of the trade-offs between different options and help us choose the best engine for our needs.
    • To facilitate easy comparison, we'll generate a comparison report in Markdown table format. This report will summarize the key metrics for each engine and highlight their strengths and weaknesses.

  • [ ] Validate EXIF descriptions accuracy

    • We'll extract the ImageDescription field from processed images and use Claude to determine whether the description accurately matches the image.
    • Claude will be asked: "Does this description accurately match the image?"
    • Any mismatches will be flagged for review.

  • [ ] Rate compression quality

    • We'll show Claude original vs compressed images side-by-side.
    • Claude will be asked: "Rate e-ink readability (1-10) of compressed image".
    • We need to ensure a score ≥8 for all compressed images. This ensures our readability standards are met.

  • [ ] Generate quality report

    • We'll aggregate all validation results into a single report.
    • The output will be a validation_report.json file.
    • The report will include pass/fail status, scores, and recommendations.

Acceptance Criteria

Here's what we need to achieve to consider this project a success:

  • ✅ Test dataset with 20 images and ground truth
  • ✅ Claude vision judge validates OCR with ±10% accuracy
  • ✅ Comparison report shows Groq > DeepSeek > Tesseract for handwriting
  • ✅ All EXIF descriptions rated ≥8/10 for accuracy
  • ✅ All compressed images rated ≥8/10 for e-ink readability

Dependencies

  • Blocked by: #9 (OCR Engine Integration), #18 (Image Optimization)
  • Blocks: None (QA tool, optional)
  • Related: #14 (Testing Framework)

Implementation Notes

Here's a snippet of the Python code, illustrating how to implement the Claude vision judge prompt:

# Example Claude vision judge prompt
{
  "model": "claude-3-7-sonnet-20250219",
  "messages": [{
    "role": "user",
    "content": [
      {"type": "image", "source": {"type": "base64", "data": "..."}},
      {"type": "text", "text": "Rate OCR accuracy (1-10): '{extracted_text}'"}
    ]
  }]
}

# Expected response
{
  "accuracy": 9,
  "completeness": 8,
  "formatting": 7,
  "reasoning": "OCR correctly extracted 95% of text..."
}

This is just an example, of course. The exact implementation will depend on the specific API we're using to interact with Claude.

Time Estimate

3 days

Priority

P2 - Nice to have, QA tool (not blocking MVP)