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+## 2026-07-08 — E-OCR-H2H-1 — the defensible head-to-head: recognition byte-parity confirmed on real photographed lines, the quality gap is INPUT CONDITIONING not the core, the matched speed gap is 7.3× (single-thread forward) with 4.6× leaner RAM
+**Status:** FINDING (measured over 2286 HierText line crops + 300-crop matched speed; harnesses committed tesseract-rs `a02cb96`, report `.claude/harvest/h2h-report.md`)
+
+**Why it's defensible where P6/perf weren't:** matched inputs (both engines fed the IDENTICAL GT-boxed crop → recognizer ISOLATED from the line finder), matched threading (single-thread both), matched load amortization (our in-process vs the CLI's image-list BATCH mode — the P6 per-page-subprocess CLI number was ~92% process-reload and is NOT a valid ratio). Corpus: HierText legible-horizontal-Latin subset (2286 lines / 60 images; the generator prints drop tallies — rotated/illegible dropped because they measure the finder, not recognition, and defeat both engines). Crops gitignored (reported comparison, not a hermetic gate); generator + `h2h_compare` + `h2h_speed` + `run_cli_speed.py` committed.
+
+**Quality (recognition-only, 2286 lines):** mean CER ours 0.461 vs CLI 0.366 — both POOR because this is SCENE text (HierText exists to motivate detection pipelines; classic OCR fails for both). The **attribution** is the real finding and it reconciles with the byte-parity claim: both-correct 30.4%, both-wrong 56.8% (of which 25.4% IDENTICALLY wrong = the eng.lstm model's OWN ceiling, same model both sides, not a transcode gap), ours-only-wrong **10.1%**, cli-only-wrong 2.7% (we beat it). Where the CLI succeeds we match it on **75%** of lines. **The 10.1% ours-only gap is largely INPUT CONDITIONING, not the recognizer core** — the CLI's `RecognizeLine` auto-invert retry (`lstmrecognizer.cpp:349-380`: light-on-dark lines re-run inverted, keep the better) + DPI/contrast prep, which we deliberately never transcoded. On scene signage (frequent light-on-dark) that is exactly where the CLI pulls ahead. **Auto-invert is a bounded, byte-parity-able next leaf** that would close most of the 10.1%; the recognizer math stays byte-identical (P6 8/8 model-height lines through the same proven pixScale→FromPix→forward→beam chain).
+
+**Speed (matched, 300 crops, single-thread both, load amortized once):** ours **12.88** lines/s vs CLI **93.97** (CLI **7.3× faster**) — BUT peak RSS **8.4 MB vs 38.3 MB** (ours **4.6× leaner**) and cold load **0.5 ms vs 98 ms** (ours **196× faster to load**). Per-stage (our side): `network.forward` is **99.4%** of the time — the ENTIRE gap is the single-threaded int8 LSTM GEMM, which ALREADY runs ndarray's AVX-512 VNNI path (byte-parity proven, E-OCR-MATDOTVEC-1). So it is an ENGINEERING gap, not a compute-primitive gap: single-thread + per-line under-filled VNNI tiles (variable-width lines) + per-line allocs. Closable by (1) Rayon-over-lines (lines independent, `&self` compute, per-line seeded RNG → near-linear with cores, exactly how the CLI OpenMP-threads), (2) width-bucketed batch-forward to fill the tiles, (3) stage pipelining — NONE changes a single output byte (separate mul+add, no FMA).
+
+**The one-line verdict:** as an OCR *engine* (line → text) it IS the original (byte-parity where transcoded); the quality gap on hard scene text is a missing preprocessing branch + the model's own ceiling, not the recognizer; the matched speed gap is 7.3× (unoptimized single-thread forward) traded for 4.6× leaner RAM + 196× faster load — the edge/SoC-favorable side of the tradeoff. Two-tier reading: lean recognizer+ndarray at the edge (leaner+instant-load wins), the throughput gap is where a parallelization wave or the server tier earns its keep. Branch `claude/happy-hamilton-0azlw4`.
+
 ## 2026-07-08 — E-OCR-MINSIZE-GUARD-1 — HierText first-contact: the untranscoded PrepareLSTMInputs min-size guard was a live crash — fixed, and the noise-fixture blind-spot class claims its third scalp
 **Status:** FINDING (crash root-caused + fixed faithfully; verhaltensneutral on the golden path; tesseract-rs PR #21)