Module image analysis Roadmap

Image Analysis Module Roadmap

Current Status

Production-ready image analysis with multi-engine support for OCR and object detection. The image analysis module provides computer vision capabilities integrated into ThemisDB's content indexing pipeline, with pluggable backends, feature extraction, and result caching.

Milestone: Phase 3 deliverables complete. OCR via Tesseract and object detection via YOLOv8 ONNX hardened and deployed to production.

• Tesseract OCR plugin with text extraction (Phase 3) → COMPLETE
• YOLOv8 ONNX object detection plugin (Phase 3) → COMPLETE
• Feature extraction and caching layer → COMPLETE
• Multi-backend abstraction and lifecycle management → COMPLETE

Completed Initiatives

Phase 1-3 Delivery (Q3 2026) - COMPLETE ✓

All image analysis infrastructure implemented and integrated. Module ready for production deployment.

Implementation Phases (Completed 2026-08-10)

Phase 1: Design & API Contract ✓ COMPLETE

Objective: Define API contracts, backend abstraction, and feature extraction semantics.

Deliverables:

• include/image_analysis/image_processor.h – Core image processing API
• include/image_analysis/feature_extractor.h – Feature extraction interface
• include/image_analysis/image_cache.h – Result caching contract
• Plugin manifest and backend registration mechanism
• Error taxonomy (image errors: E6200–E6299)

API Contracts:

• ImageProcessor — Main entry point for image analysis (OCR, detection)

  • processImage(path) → ImageAnalysisResult
  • extractText() → OCRResult with confidence scores
  • detectObjects() → DetectionResult with bounding boxes

• FeatureExtractor — Vector embedding generation

  • extractFeatures(image) → EmbeddingVector
  • Supports multiple embedding models

• ImageCache — Result caching with TTL

  • get(key) → Optional<CachedResult>
  • put(key, value, ttl_ms) → Result<>

Status: ✓ COMPLETE

Phase 2: Core Implementation ✓ COMPLETE

Objective: Implement OCR and object detection with production-grade error handling.

Deliverables:

• tesseract_ocr_plugin.cpp – Tesseract integration with text extraction

  • Language auto-detection and multi-language support
  • Layout analysis and text region segmentation
  • Confidence scoring for extracted text
  • Error handling for corrupted images

• yolov8_onnx_plugin.cpp – YOLOv8 object detection via ONNX

  • Class detection with confidence thresholds
  • Bounding box computation and NMS (non-maximum suppression)
  • Batch processing support
  • ONNX model loading and inference

Performance Targets:

• OCR throughput: ≥ 10 images/sec on typical hardware
• Object detection throughput: ≥ 5 images/sec
• Memory per image: < 50 MB (including model caches)
• Cache hit rate target: > 80% for typical workloads

Status: ✓ COMPLETE

Phase 3: Error Handling & Edge Cases ✓ COMPLETE

Objective: Handle various image formats, corrupted data, and backend unavailability.

Deliverables:

• Image format validation and conversion
• Corrupted image detection and fallback handling
• Backend unavailability graceful degradation
• Timeout enforcement for long-running operations
• Structured error reporting with diagnostic context

Error Scenarios:

• E6200 – Unsupported image format
• E6201 – Image corrupted or invalid
• E6202 – Backend initialization failed
• E6203 – Processing timeout exceeded
• E6204 – Insufficient memory for image

Status: ✓ COMPLETE

Phase 4: Tests ✓ COMPLETE

Objective: Comprehensive testing of OCR, detection, and caching.

Test Suite:

• Unit tests for OCR text extraction
• Object detection accuracy validation
• Feature extraction vector correctness
• Cache hit/miss semantics
• Backend lifecycle management
• Error handling and timeout scenarios

Test Coverage:

• src/image_analysis coverage via focused test suites
• Integration tests with content indexing pipeline
• Performance regression tests on standard datasets

Status: ✓ COMPLETE

Phase 5: Performance & Hardening ✓ COMPLETE

Objective: Optimize critical paths and validate production readiness.

Deliverables:

• OCR throughput optimization (batch processing)
• Object detection latency profiling
• Memory usage optimization for model caching
• Cache efficiency validation
• Cross-backend performance comparison

Performance Gates:

• OCR P99: < 100 ms per image
• Detection P99: < 200 ms per image
• Cache lookup: < 1 ms
• Memory overhead: < 10% of total heap

Status: ✓ COMPLETE

Phase 6: Documentation & Acceptance ✓ COMPLETE

Objective: Complete API documentation, integration guide, and operator runbook.

Deliverables:

• Doxygen comments for all public APIs
• Backend integration guide (adding custom vision engines)
• Configuration parameter documentation
• Performance tuning guide
• Troubleshooting runbook

Documentation:

• README.md – Module overview and quick-start
• ARCHITECTURE.md – Design rationale and backend abstraction
• FUTURE_ENHANCEMENTS.md – Planned features (video support, custom models)

Status: ✓ COMPLETE

Production Readiness Checklist

• Phase 1 API contracts frozen
• Phase 2 core implementation (OCR + detection) complete
• Phase 3 error handling comprehensive
• Phase 4 test suite ≥ 70% code coverage
• Phase 5 benchmarks pass all gates
• Phase 6 documentation complete
• Security review passed (no remote code execution vectors)
• Performance validation
• Integration testing with content indexing
• Operational runbook complete

Known Issues & Limitations

1. Single-Backend Limitation – Only Tesseract and YOLOv8 currently supported; plugin architecture enables extension
2. No Video Support – Only static images; video frame extraction is out of scope
3. No Model Fine-Tuning – Uses pre-trained models; custom training not supported in this module
4. Language Limitations – Tesseract multi-language support depends on installed language packs

Breaking Changes

None. APIs frozen at v1.x.

Module Statistics

• Total LOC (Source): ~500 LOC across implementation files
  • tesseract_ocr_plugin.cpp: ~250 LOC
  • yolov8_onnx_plugin.cpp: ~250 LOC
• Public Headers: 3 (image_processor.h, feature_extractor.h, image_cache.h)
• Supported Formats: JPEG, PNG, TIFF, BMP, WebP
• Error Codes: E6200–E6299 (reserved)

Program Execution Model — Wave Context

This module is a contributing module in the program-level Wave A → B → C → D execution model. It does not own a primary wave deliverable but must remain release_critical-green throughout all waves.

See [[../../ROADMAP.md|ROADMAP]] for the full wave model and exit criteria.