Intelligent Document Processing pipelines first run OCR to turn scanned pixels into text tokens with bounding boxes, then a layout-aware model (like LayoutLMv3) fuses text, position and visual features to classify each region into a structured field.
P(correct) = threshold / (threshold + noise)
throughput ~ heads * scan_rate
- OCR tokens — text/box regions detected on the page, waiting to be classified.
- Classifier heads — parallel model passes scanning the page and attempting to label each token (key, value, header, table cell).
- Confidence threshold — how much model certainty is required before a label is accepted as correct.
- Layout noise — skew, low DPI, dense tables or handwriting that lowers effective confidence and slows extraction.
Classified tokens are pulled out as structured key-value entities and stacked into the results shelf above the page — the extraction pipeline's actual output.