OCR-BENCHMARK.md

OCR Benchmark — results & history

Result tables and validation history for the OCR benchmark tooling. How to run the tools (ocr-bench-run.py, ocr-vllm-judge.py, ocr-human-eval.py) lives in CLAUDE.md → "Internal tooling"; this file is the accumulated evidence.

Model registry (as benchmarked)

Slug	Model	Size	GPU	Notes
glm-ocr	zai-org/GLM-OCR	0.9B	l4x1
deepseek-ocr	deepseek-ai/DeepSeek-OCR	4B	l4x1	auto --prompt-mode free (no grounding tags)
lighton-ocr-2	lightonai/LightOnOCR-2-1B	1B	a100-large
dots-ocr	rednote-hilab/dots.ocr	1.7B	l4x1	stable vLLM (>=0.9.1)

Run 1 — NLS Medical History (2026-02-14, pilot)

NationalLibraryOfScotland/medical-history-of-british-india, 10 samples, seed 42. Judge: Qwen2.5-VL-72B via Inference Providers. Historical English, degraded scans.

• ELO (pairwise, 5 samples): DoTS 1540 (67%) · DeepSeek 1539 (57%) · LightOnOCR-2 1486 (50%) · GLM 1436 (29%)
• Pointwise (5): DeepSeek 5.0 · GLM 4.6 · LightOnOCR-2 4.4 · DoTS 4.2
• Key finding: DeepSeek's --prompt-mode document emits grounding tags (<|ref|>/<|det|>) the judge penalises heavily; switching to --prompt-mode free moved it last→top-2 (now the registry default).
• Caveat: 5 samples is far too few for stable rankings.

Run 2 — Rubenstein Manuscript Catalog (2026-02-15, first full run)

biglam/rubenstein-manuscript-catalog, 50 samples, seed 42. Judge: jury of Qwen2.5-VL-7B + Qwen3-VL-8B on A100 (ocr-vllm-judge.py). ~48K typewritten + handwritten cards (Duke, CC0).

ELO (50 samples, 300 comparisons, 0 parse failures):

Rank	Model	ELO	W	L	T	Win%
1	LightOnOCR-2-1B	1595	100	50	0	67%
2	DeepSeek-OCR	1497	73	77	0	49%
3	GLM-OCR	1471	57	93	0	38%
4	dots.ocr	1437	70	80	0	47%

Job times (50 samples): dots 5.3 min (L4) · deepseek 5.6 (L4) · glm 5.7 (L4) · lighton 6.4 (A100).

Findings: LightOnOCR-2 dominates on manuscript cards (very different from the NLS pilot) — rankings are dataset-dependent; a jury of small models works well (0 parse failures via vLLM structured output); 50 samples gives meaningful separation.

Run 3 — UFO-ColPali (2026-02-15, cross-dataset validation)

davanstrien/ufo-ColPali, 50 samples, seed 42. Judge: Qwen3-VL-30B-A3B on A100 (updated prompt). Mixed modern documents.

ELO (50 samples, 294 comparisons):

Rank	Model	ELO	W	L	T	Win%
1	DeepSeek-OCR	1827	130	17	0	88%
2	dots.ocr	1510	64	83	0	44%
3	LightOnOCR-2-1B	1368	77	70	0	52%
4	GLM-OCR	1294	23	124	0	16%

Human validation (30 comparisons): DeepSeek #1 (matches judge), LightOnOCR-2 #3 (matches). Middle pack (GLM, dots) shuffled between human and judge.

Cross-dataset comparison (human-validated)

Model	Rubenstein Human	Rubenstein Kimi	UFO Human	UFO 30B
DeepSeek-OCR	#1	#1	#1	#1
GLM-OCR	#2	#3	#2	#4
LightOnOCR-2	#4	#2	#3	#3
dots.ocr	#3	#4	#4	#2

Conclusion: DeepSeek-OCR is consistently #1 across datasets and eval methods; middle-pack rankings are dataset-dependent. (NLS pilot omitted — 5 samples / 72B API judge, not comparable with the newer methodology.)

Judge validation — ocr-vllm-judge.py (2026-02-15)

• Test 1 (single judge, 1 sample, L4): Qwen2.5-VL-7B, 6/6 comparisons, 0 parse failures, ~3 min.
• Test 2 (jury of 2, 3 samples, A100): Qwen2.5-VL-7B + Qwen3-VL-8B, 15/15, 0 failures; GPU cleanup between models OK; majority-vote aggregation working ([2/2] unanimous, [1/2] split).
• Test 3 (full, 50 samples, A100, Rubenstein): 300/300 comparisons, 0 parse failures; clear ELO separation. First saved dataset: davanstrien/ocr-bench-rubenstein-judge.

Structured output via a compatibility shim: StructuredOutputsParams (vLLM ≥0.12) → GuidedDecodingParams (older) → prompt-based fallback. Position bias mitigated by A/B randomisation. A100 recommended for jury mode.

Human eval — ocr-human-eval.py first validation (Rubenstein, 30 annotations)

Tested 3 judge configs against 30 human annotations. Kimi K2.5 (170B) via Novita + the updated prompt is the only judge to match the human's #1 (DeepSeek-OCR); it's now the default in ocr-jury-bench.py. Small models (7B/8B/30B) overrate LightOnOCR-2 (bias toward its commentary style); the updated prompt (faithfulness > completeness > accuracy) helps, but model size is the bigger factor.