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docs(ocr): add two-tier Vision→OCR workflow to backlog + comparison test
Added to OCR_BACKLOG.md (Medium Priority): - Two-tier workflow: Vision classification → OCR deep analysis - Tier 1 (Vision): Quick, cheap image classification - Tier 2 (OCR): Detailed data extraction on demand - Triggers: Image markers, complex tables, user choice - Benefits: Cost-effective, flexible, user-controlled depth - Dedicated Mistral OCR API wrapper (to be built) - Use client.ocr.process() not chat.complete() - Model: mistral-ocr-2512 (OCR 3) - Features: structured output, tables, headers/footers - Note: Current wrapper is Vision API (good for classification) Created OCR_COMPARISON_TEST.md: - Detailed comparison: Claude Vision vs Mistral Vision vs Mistral OCR - Test case: N3290x Design Guide, Page 890 timing diagram - Results: Vision good for "what is this?", OCR best for precise extraction - Recommended workflow: Vision triage → OCR deep dive (optional) - Cost analysis: Vision ~$0.003, OCR ~$0.002 per page - Both should offer persistent caching Clarifies the Vision/OCR API confusion discovered during testing. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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‎OCR_BACKLOG.md‎

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### Medium Priority
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- [ ] **Two-tier Vision → OCR workflow** 🆕
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- **Tier 1: Vision Classification (quick, cheap)**
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- Classify image type: "timing diagram", "table", "schematic", etc.
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- Provide context: "4 signals, technical drawing"
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- Ask: "Want more details?" → proceed to Tier 2
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- **Tier 2: OCR Deep Analysis (detailed, expensive)**
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- Extract all labels, values, annotations
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- Precise technical data: "VDD33: 3.3V @ t=0ms, threshold: 1.62V"
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- **Triggers:**
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- Image markers: `[IMAGE n: ...]` → offer Vision classification
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- Complex tables: Detected via text extraction → offer OCR
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- User choice: Both tiers should be optional
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- **Benefits:**
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- Cost-effective: Vision for triage, OCR only when needed
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- Flexible: User controls depth of analysis
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- **Implementation:**
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- Vision wrapper (existing: chat.complete)
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- OCR wrapper (new: ocr.process with mistral-ocr-2512)
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- Workflow orchestration tool/skill
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- [ ] **Build dedicated Mistral OCR API wrapper** 🆕
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- Use `client.ocr.process()` instead of Vision API
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- Model: `mistral-ocr-2512` (OCR 3)
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- Endpoint: `/v1/ocr`
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- Features: table_format, extract_header/footer, include_image_base64
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- Structured output: markdown, HTML tables, images
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- Price: $2/1000 pages ($1 with Batch API)
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- **Note:** Current wrapper uses Vision API (chat.complete) - good for classification, not OCR
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- [ ] **Add troubleshooting guide** (`docs/troubleshooting/ocr.md`)
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- Common errors (missing endpoint, auth failures)
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- Response format validation errors

‎OCR_COMPARISON_TEST.md‎

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# OCR API Comparison Test
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**Date:** 2025-12-22
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**Document:** N3290x_Design_Guide_A1.pdf, Pages 889-890
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**Purpose:** Compare Vision API vs OCR API for technical diagram analysis
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## Test Case: Power-on Sequence Timing Diagram
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**Location:** Page 890, Image 1 (918x482px)
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**Content:** Technical timing diagram with voltage signals, thresholds, and timing parameters
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---
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## Method 1: Claude Vision (Native)
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**Process:** pdf-reader-mcp → Image extraction → Claude Sonnet 4.5 Vision
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**Result:**
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### Analysis:
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- **Identified as:** Power-on Sequence Timing-Diagramm
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- **Signals detected:**
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1. VDD33 (blau) - 3.3V IO Power
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2. 1.8V Core Power (türkis)
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3. RESET (rot gestrichelt)
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4. Internal RESET (magenta gestrichelt)
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- **Key parameters extracted:**
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- Threshold: 1.62V
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- Threshold: VDD33/2
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- Timing: "More than 4T where T is XTAL cycle"
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- Duration: 75ms
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- Label: "Valid power on setting value"
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- **Axes:**
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- Y-axis: Voltage (V)
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- X-axis: Time (mS)
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**Quality:** ✅ Accurate, comprehensive technical understanding
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**Cost:** ~$0.01-0.02 per image (Claude API pricing)
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**Cache:** ❌ No persistent cache (not part of pdf-reader-mcp OCR cache)
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---
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## Method 2: Mistral Vision API (via wrapper)
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**Process:** pdf-reader-mcp → Image extraction → Mistral Vision wrapper → mistral-large-2512
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**Current Status:** ✅ Wrapper built and tested
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**API Used:** `client.chat.complete()` with vision
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**Model:** mistral-large-2512
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**Expected Result:** Similar to Claude Vision
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- Semantic understanding
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- Identifies diagram type
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- General description of signals
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**Quality:** Expected ✅ Good for classification
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**Cost:** ~$0.002-0.003 per image (Mistral Vision pricing)
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**Cache:** ✅ Persistent disk cache (`N3290x_Design_Guide_A1_ocr.json`)
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**Note:** This is **Vision API**, not **OCR API** - good for "what is this?" not "extract all labels"
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---
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## Method 3: Mistral OCR API (NOT YET IMPLEMENTED)
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**Process:** pdf-reader-mcp → Image/PDF → Mistral OCR wrapper → mistral-ocr-2512
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**Current Status:** ❌ Not implemented (Vision wrapper built instead)
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**API Needed:** `client.ocr.process()`
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**Model:** `mistral-ocr-2512` (OCR 3)
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**Endpoint:** `/v1/ocr`
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**Expected Features:**
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- Structured output: `.markdown`, `.tables[]`, `.images[]`
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- Precise text extraction from technical diagrams
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- Table detection with HTML/markdown output
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- Header/footer extraction
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**Expected Result for our diagram:**
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```json
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{
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"markdown": "VDD33\n1.8V Core Power\nRESET\nInternal RESET\n...",
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"labels": [
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"Voltage (V)",
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"Time (mS)",
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"1.62V",
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"VDD33/2",
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"More than 4T where T is XTAL cycle",
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"75ms",
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"Valid power on setting value"
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]
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}
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```
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**Quality:** Expected ✅✅ Best for precise data extraction
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**Cost:** $2 per 1,000 pages = $0.002 per page ($1 with Batch API)
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**Cache:** ✅ Persistent disk cache (same as Vision wrapper)
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---
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## Comparison Summary
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| Method | API Type | Quality | Cost/Image | Cache | Best For |
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|--------|----------|---------|------------|-------|----------|
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| **Claude Vision** | Vision | ✅ Excellent | ~$0.01-0.02 | ❌ No | Semantic understanding, complex analysis |
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| **Mistral Vision** | Vision | ✅ Good | ~$0.002-0.003 | ✅ Yes | Quick classification, "what is this?" |
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| **Mistral OCR** | OCR | ✅✅ Best | ~$0.002 | ✅ Yes | **Precise data extraction, technical diagrams** |
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---
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## Recommended Workflow: Two-Tier Approach
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### Tier 1: Vision Classification (Quick Triage)
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**Tool:** Mistral Vision wrapper (existing)
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- "This is a timing diagram with 4 signals"
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- "Complex table with 12 rows"
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- **Cost:** Low (~$0.003)
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- **Speed:** Fast
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- **Decision:** "Interesting? → Proceed to OCR"
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### Tier 2: OCR Deep Analysis (On Demand)
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**Tool:** Mistral OCR wrapper (to be built)
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- "VDD33: 3.3V, rises from 0V at t=0ms"
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- "Threshold: 1.62V (VDD33/2)"
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- "Timing constraint: >4T where T=XTAL cycle"
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- "Duration: 75ms until valid power-on"
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- **Cost:** Low (~$0.002)
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- **Speed:** Moderate
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- **Trigger:** User requests details
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### Benefits:
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- 💰 Cost-effective: Vision for triage, OCR only when needed
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- ⚡ Fast: Quick overview without deep analysis
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- 🎯 Flexible: User controls analysis depth
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- 💾 Cached: Both results persist in .json files
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---
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## Action Items
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- [x] Build Mistral Vision wrapper (completed)
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- [ ] Build Mistral OCR wrapper (`client.ocr.process()`)
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- [ ] Implement two-tier workflow
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- [ ] Add Vision classification as optional step in pdf-reader-mcp
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- [ ] Document both approaches in guide
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---
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## Technical Notes
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### Current Mistral Vision Wrapper
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- ✅ Working: POST /v1/ocr endpoint
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- ✅ Uses: `client.chat.complete()` with vision
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- ✅ Accepts: Base64 images, data URIs
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- ✅ Returns: `{ text, language }`
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- ⚠️ Limitation: Vision API, not OCR API - good for understanding, not extraction
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### Needed: Mistral OCR Wrapper
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- ❌ Not built yet
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- Should use: `client.ocr.process()`
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- Should accept: PDFs, base64 images
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- Should return: Structured data (markdown, tables, images)
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- Features: table_format, extract_header/footer, include_image_base64
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### Why Both?
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- **Vision:** Semantic understanding ("This is a Power-on sequence diagram")
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- **OCR:** Data extraction ("VDD33=3.3V, t=75ms, threshold=1.62V")
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- **Together:** Complete analysis pipeline
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---
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**Conclusion:** For technical diagrams like our timing diagram, the ideal approach is:
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1. Quick Vision classification to understand context
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2. Deep OCR analysis to extract precise values
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3. Both cached for future reference

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