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davanstrien
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isl-finepdfs-ocr-results

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train · 3 rows

model string	elo int64	params string	wins int64	losses int64	ties int64	win_pct int64	elo_low int64	elo_high int64
rednote-hilab/dots.ocr	1,613	1.7B	14	5	1	70	1,513	1,769
deepseek-ai/DeepSeek-OCR	1,462	4B	8	11	1	40	1,341	1,586
zai-org/GLM-OCR	1,425	0.9B	7	13	0	35	1,282	1,534

OCR Bench Results: isl-finepdfs-ocr

VLM-as-judge pairwise evaluation of OCR models. Rankings depend on document type — there is no single best OCR model.

Leaderboard

Rank	Model	Params	ELO	95% CI	Wins	Losses	Ties	Win%
1	rednote-hilab/dots.ocr	1.7B	1613	1513–1769	14	5	1	70%
2	deepseek-ai/DeepSeek-OCR	4B	1462	1341–1586	8	11	1	40%
3	zai-org/GLM-OCR	0.9B	1425	1282–1534	7	13	0	35%

Details

Source dataset: davanstrien/isl-finepdfs-ocr
Judge: Qwen3.5-35B-A3B
Comparisons: 30
Method: Bradley-Terry MLE with bootstrap 95% CIs

Configs

load_dataset("davanstrien/isl-finepdfs-ocr-results") — leaderboard table
load_dataset("davanstrien/isl-finepdfs-ocr-results", name="comparisons") — full pairwise comparison log
load_dataset("davanstrien/isl-finepdfs-ocr-results", name="metadata") — evaluation run history

Generated by ocr-bench

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