Technology - Vintage Scanner

🤖 The 13 AI Detection Systems

1️⃣ ML Kit Detection

Google on-device AI: Image labeling, object detection, OCR text recognition

2️⃣ Custom Vintage Model

Enhanced ML trained on 70+ vintage categories with keyword matching

3️⃣ Color Era Detection

Analyzes 6 dominant colors, matches to 8 historical era palettes

4️⃣ Enhanced OCR

Extracts years, 40+ brands, 30+ materials, hallmarks, serial numbers

5️⃣ Material Texture

Real wood vs particle board, genuine leather vs synthetic detection

6️⃣ Patina & Age

Metal oxidation, wood darkening, silver tarnish, ceramic crazing, wear patterns

7️⃣ Pattern Recognition

Geometric (Art Deco), Floral (Victorian), Atomic (Mid-Century) patterns

8️⃣ 7-Category System

Specialized detection: Furniture, Jewelry, Electronics, Clothing, Art, Cars, Books

9️⃣ Multi-Engine Framework

Weighted consensus voting combining all AI systems

🔟 TensorFlow Lite

Custom vintage models with GPU acceleration, <100ms inference

1️⃣1️⃣ Cloud AI

Google Cloud Vision for reverse image search, web entity detection

1️⃣2️⃣ Database Cross-Reference

10,000+ verified items from museums and auction houses

1️⃣3️⃣ Expert Network

5 certified experts for professional verification

📊 How We Achieve 90-95% Accuracy

Each AI system contributes to the final authentication score. We use weighted consensus voting where more reliable systems (like TensorFlow custom models) have higher weight. The ML feedback loop continuously learns from user corrections, making the system smarter over time.

Accuracy Breakdown:

✓ ML Kit Detection: 60-70% (base)
✓ Color Era + OCR: +10-15%
✓ Material + Patina + Pattern: +10-15%
✓ TensorFlow + Cloud AI + Database: +10-15%
= 90-95% Total Accuracy

🔬 Our Technology