The 3¢ Washington stamp of 1851–1857 is one of the most intensively studied stamps in American philately. Dr. Carroll Chase was the first to plate it systematically, and his landmark work established the foundation for everything that followed. Over the decades, a small and dedicated community of specialists — including Wilbur F. Amonette, Richard C. Celler, and others — extended, corrected, and deepened that foundation, each building on the work of those who came before. Today that tradition continues through J. Bryan O'Doherty's stampplating.com, which brought the accumulated knowledge of the field into the digital age.
Printed from 13 engraved plates, each containing 200 individual positions, every one of the 2,600 positions has a unique combination of characteristics resulting from the engraving process itself — making each position identifiable from the stamp alone.
PlateAI works on Scott #10, 10A, 11, 11A, 25, and 25A — Types I and II only. Type III and IV stamps (#26, 26A) were printed from different plates and are not supported.
PlateAI brings machine learning to this problem. Given a scan of a stamp, the system analyzes specific regions of the stamp, and compares the visual signature against reference images from all 2,600 positions. It returns a ranked list of candidates with a confidence assessment, and tools to compare your stamp directly against vetted reference images side by side.
Under the hood, PlateAI uses a multi-branch convolutional neural network (CNN) trained with triplet loss — a technique that teaches the model to recognize visually similar positions as similar and dissimilar positions as different. Each branch of the network specializes in a different region of the stamp, and their outputs are combined into a single similarity score used to rank candidates.
Trained on approximately 19,000 scans from a wide range of collections, the model achieves 96.7% Top-1 accuracy on clean four-margin stamps, with the correct position appearing in the top ten essentially every time. On three-margin stamps Top-1 accuracy drops to 91.0%; on two-margin stamps Top-1 accuracy drops to 82.2%.
The model is nearly perfect in predicting the Scott number on clean scans.
Whether you are new to plating, an experienced specialist, or simply curious about what machine learning can do with a 170-year-old challenge — PlateAI is designed to be a useful tool and a starting point for exploration, not a final word. Visual comparison and philatelic judgment remain essential.
Stamp scans uploaded to PlateAI may be retained on private storage and used to improve future versions of the model. No other information is collected.
PlateAI was conceived, designed, and built by David Fussichen, with the encouragement, expertise, and generosity of Robert J. Lampert and J. Bryan O'Doherty. Gary Schrader, Elvin Fritz, and Charles Temple were early users whose feedback on the tool, and broader perspective on plating, shaped the project as it developed.
Apply narrows the current list. Plate! re-runs identification with current filters.
Plate the 1851–57 3¢ Washington
Drop a stamp scan here, or click to upload
Best results: 1200 DPI flatbed scan
Scott 10, 10A, 11, 11A, 25, or 25A
Interface and model version history
| Top-1 | Top-3 | Top-5 | Top-10 | Top-15 | |
|---|---|---|---|---|---|
| 4-margin | 96.7% | 99.3% | 99.6% | 100% | 100% |
| 3-margin | 91.0% | 96.9% | 97.9% | 99.1% | 99.6% |
| 2-margin | 82.2% | 92.3% | 94.9% | 97.5% | 98.6% |
| Top-1 | Top-3 | Top-5 | Top-10 | Top-15 | |
|---|---|---|---|---|---|
| 4-margin | 95.6% | 98.7% | 99.3% | 98.9% | 100% |
| 3-margin | 92.5% | 97.5% | 98.4% | 99.3% | 99.8% |
| 2-margin | 77.0% | 88.8% | 92.2% | 95.2% | 96.7% |
Position Explorer is independent of AI identification. Filters apply, but no stamp needs to be loaded or plated. Click any eligible position to compare reference stamps.
| # | Position | Plate | Relief | Type | Scott | Score | ✓ |
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