KnowledgeMCP

# Specification Quality Checklist: Smart OCR Implementation **Feature ID**: 003-smart-ocr **Date**: 2025-10-27 **Reviewer**: Automated Validation --- ## Validation Results ### ✅ Testable and Unambiguous Requirements **Status**: PASS All functional requirements include clear acceptance criteria: - FR-001: Text quality detection with specific thresholds (100 chars, 70% alphanumeric) - FR-002: Automatic OCR fallback with clear trigger conditions - FR-003: Async processing with observable task states - FR-004: Accept all results (no ambiguity) - FR-005: Immediate cleanup with try/finally guarantee - FR-006: Processing method tracking with specific metadata fields - FR-007: Force OCR with boolean parameter - FR-008: PDF to image conversion with defined output format - FR-009: Error handling with specific failure behaviors - FR-010: Test case with concrete document and success criteria **Evidence**: Each requirement has measurable acceptance criteria that can be verified through testing. --- ### ✅ Complete User Scenarios **Status**: PASS Four comprehensive scenarios provided: 1. **Scenario 1**: Scan-only PDF processing (primary use case) 2. **Scenario 2**: Native PDF with good text (contrast case) 3. **Scenario 3**: Force OCR mode (configuration testing) 4. **Scenario 4**: Real-world test document (Medicaid PDF) Each scenario includes: - Context and setup - Step-by-step user actions - Expected outcomes - Success verification **Evidence**: Scenarios cover main paths, edge cases, and real-world validation. --- ### ✅ Technology-Agnostic Success Criteria **Status**: PASS Success criteria focus on user-observable outcomes: - "Documents with no extractable text are successfully processed" (not "Tesseract extracts text") - "Scan-only PDFs are fully indexed and searchable" (not "OCR API returns 200") - "Processing completes asynchronously" (not "asyncio tasks run") - "No temporary files remain" (outcome, not implementation) - "Test documents process within 2 minutes" (user-facing metric) **Evidence**: Criteria measure what users experience, not how system implements it. --- ### ✅ Reasonable Assumptions **Status**: PASS 10 assumptions documented covering: - Infrastructure (Tesseract installed) - Libraries (pytesseract, PIL available) - Heuristics (text quality thresholds) - Defaults (English language, accept all results) - Resources (disk space, async support) All assumptions are: - Explicitly stated - Based on existing system capabilities - Reasonable for the domain - Verifiable during implementation **Evidence**: Assumptions section is complete and realistic. --- ### ✅ Minimal Clarification Markers **Status**: PASS **Count**: 0 [NEEDS CLARIFICATION] markers All key decisions have been resolved: - OCR accuracy handling: "Accept all results" (specified by user) - Processing mode: "Always async" (specified by user) - File cleanup: "Immediate" (specified by user) - Text quality thresholds: Defined based on existing code (100 chars, 70% alphanumeric) - Language default: English (industry standard) **Evidence**: User provided clear requirements; no critical ambiguities remain. --- ### ✅ Proper Scope Boundaries **Status**: PASS **In Scope** (7 items): - Automatic OCR detection ✓ - Fallback mechanism ✓ - Async processing ✓ - Result acceptance ✓ - File cleanup ✓ - Method tracking ✓ - Force OCR option ✓ **Out of Scope** (6 items): - Model training ✗ - Manual threshold adjustment ✗ - Image pre-processing ✗ - Multiple OCR engines ✗ - Language auto-detection ✗ - Selective file retention ✗ **Evidence**: Clear boundaries prevent scope creep while delivering complete feature. --- ### ✅ Prioritized Requirements **Status**: PASS **Must Have** (8 requirements): - FR-001: Text quality detection - FR-002: Automatic OCR fallback - FR-003: Async processing - FR-004: Accept all results - FR-005: Immediate cleanup - FR-006: Method tracking - FR-008: PDF to image conversion - FR-009: Error handling - FR-010: Testing validation **Should Have** (2 requirements): - FR-007: Force OCR configuration - NFR-001: Performance targets - NFR-003: Observability **Evidence**: Critical path clearly identified; nice-to-haves separated. --- ### ✅ Documented Dependencies **Status**: PASS **Technical Dependencies** (4 items): 1. Tesseract OCR (system package) 2. pytesseract (Python wrapper) 3. pdf2image or pymupdf (PDF conversion) 4. PIL/Pillow (image processing) **Feature Dependencies**: None (standalone enhancement) **Evidence**: All external dependencies identified with specific purpose. --- ### ✅ Risk Assessment **Status**: PASS Four risks identified with mitigation: 1. **OCR Processing Time** (Medium/Medium) - Mitigation: Async processing, timeouts, progress indicators 2. **Temporary File Accumulation** (Low/High) - Mitigation: try/finally cleanup, periodic orphan cleanup, monitoring 3. **Poor OCR Quality** (Medium/Medium) - Mitigation: Accept all results, log confidence, future pre-processing 4. **Missing Dependencies** (Low/High) - Mitigation: Clear documentation, graceful degradation, startup checks **Evidence**: Realistic risks with practical mitigations addressing likelihood and impact. --- ### ✅ Measurable Success Metrics **Status**: PASS Six metrics defined: 1. **OCR Accuracy Rate**: 95% of scan-only docs fully indexed 2. **Processing Time**: Under 2 minutes per page 3. **Automatic Detection Rate**: 100% correct OCR triggering 4. **Search Quality**: Test queries return expected results 5. **File Cleanup**: 100% cleanup rate 6. **System Stability**: Zero OCR-related crashes All metrics are: - Quantifiable (percentages, time, counts) - Verifiable through testing - Have specific targets **Evidence**: Clear measurement plan for validating success. --- ## Overall Assessment **Final Status**: ✅ **READY FOR PLANNING** ### Summary The specification successfully meets all quality criteria: - ✅ 10/10 checklist items passed - ✅ Zero critical issues identified - ✅ No clarifications needed (user provided complete requirements) - ✅ Ready to proceed to `/speckit.plan` phase ### Strengths 1. **Concrete Test Case**: Real Medicaid PDF provides tangible validation target 2. **Clear Heuristics**: Text quality thresholds are specific and testable 3. **Well-Scoped**: Focused on automatic OCR intelligence without overreach 4. **User Requirements**: All three key decisions (accept all, async, immediate cleanup) explicitly specified ### Next Steps 1. Run `/speckit.plan` to break down requirements into implementation stories 2. Implement PDF to image conversion capability (if not existing) 3. Enhance OCR service with automatic fallback logic 4. Add processing method tracking to documents 5. Test with Medicaid PDF and validate search quality --- **Checklist Generated**: 2025-10-27 **Ready for Planning**: Yes **Blockers**: None