board: E-OCR-H2H-1 — matched head-to-head vs the CLI (recognition byte-parity, gap is conditioning; 7.3× speed / 4.6× leaner RAM)

[AdaWorldAPI]

What

Board-only companion to tesseract-rs' H2H harness PR (per the "board hygiene lands in lance-graph" iron rule): prepends E-OCR-H2H-1 to .claude/board/EPIPHANIES.md.

Records the defensible head-to-head over 2286 HierText line crops (matched inputs / threading / load amortization; recognizer isolated from the line finder):

• Quality (recognition-only): mean CER ours 0.461 vs CLI 0.366 — both poor on scene text; the attribution reconciles with byte-parity — 25.4% both-wrong-identically is the model's own ceiling (same model both sides), the 10.1% ours-only gap is largely the CLI's un-transcoded RecognizeLine auto-invert/preprocessing, not the recognizer core (P6 8/8 byte-parity holds). Auto-invert = a bounded next leaf.
• Speed (matched, single-thread, amortized load): ours 12.88 lines/s vs CLI 93.97 (7.3× slower), peak RSS 8.4 vs 38.3 MB (4.6× leaner), cold load 0.5 vs 98 ms (196× faster). network.forward = 99.4% — the gap is the single-thread int8 GEMM (already the VNNI path), closable by Rayon + batch-forward without changing a byte.
• Two-tier reading: lean recognizer + ndarray at the edge wins on RAM + load; the throughput gap is the server-tier / parallelization-wave's to earn.

🤖 Generated with Claude Code

https://claude.ai/code/session_016b33swuXE23hKtqxsHu9p1

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