Labels vs. Book Covers: Structured Extraction Tradeoffs

Daniel needs to extract structured data from product labels and book covers — serial numbers, titles, authors, and a wildcard field for anything unexpected. Should he use a general vision model like GPT-4V or build a purpose-trained pipeline? We break down the real tradeoffs: accuracy on tiny rotated text, cost at scale, offline capability, how each approach handles the novel-fields requirement, and what the actual pipeline looks like. A twelve-point accuracy gap and a forty-x latency difference make this less subtle than it seems. Episode #917346 — open it directly at myweirdprompts.com/917346