Document and Receipt OCR: Designing the Edge Pipeline
Treating OCR as “one model” is a common misconception. Results depend on the whole pipeline: detect, rectify, recognize, structure.
OCR is a pipeline, not a model
- Text detection: DBNet-class models find text lines; skew-correct perspective first;
- Recognition: quantized CRNN/SVTR on the NPU runs 20-50 ms per line;
- Structuring: template-based field extraction (invoice no., date, amount) with regex plus position checks.
98% on printed receipts is realistic; for handwriting, plan on 85% and keep a human review path.
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