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Field notes: how the pilot cuts a UT form photo into cells — morphology, projections, gated perspective — and which other sheet-splitting methods show up in industry practice.
How to choose neural network quality metrics: classification, computer vision, OCR, LLM and RAG, calibration and production indicators — what to measure so the model truly works in the product.
Field notes: a local CRNN on thickness crops, CTC, 640k parameters, the empty-cell conflict, and why a Transformer is not the first step.
Third UT-pilot note: error diagnosis, one lever at a time, epochs, the dataset, and when the model should stop being squeezed for points.
UT pilot sequel: five cell kinds, ink, ditto-up-the-column, strike-through, and why heuristics still do not replace a tiny classifier.
Field notes from a pilot: computer vision, OCR, vision LLMs, confidence, and operator review before ERP — not a finished production guide.
A practical path from single-digit classification to sequence recognition on real paper forms: CNN, CTC, CRNN, TrOCR, labels from your database, experiments, and a production pipeline without the wrong model.
Flowcharts, UML, BPMN, network topologies, and CAD drawings: why PNG ≠ diagram, what OCR, vectorization, CAD, computer vision, and VLMs deliver in 2025–2026, and which pipeline actually works.
