By skyforbes **The Challenge:**
Needed to extract structured data from invoice photos with poor quality (blurry, skewed, bad lighting). Traditional OCR kept failing.
**What I Tested:**
**Traditional OCR (Tesseract):**
– Accuracy: ~55% on low-quality images
– Needed lots of preprocessing
– Broke easily on varying formats
**Gemini Vision API:**
– Accuracy: ~92% on same images
– Handled poor quality remarkably well
– Better at understanding document structure
– Extracted fields consistently
**Key Takeaway:**
Vision models are WAY better than traditional OCR for real-world messy documents. The context understanding makes a huge difference.
**Implementation:**
Simple pipeline: Photo → Gemini Vision API with structured prompts → Validation → Clean data output
Prompt engineering was critical – explicitly defining the output format (JSON schema) and validation rules significantly improved consistency.
**Anyone else using AI vision for document processing?**
Curious what models you've tested and how they compare. Would love to hear experiences with GPT-4V or Claude 3 for similar use cases.