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Six jobs. Different evidence.

01 / Measured by CodeSOTA

Scores with execution evidence.

Handwriting transcription and visual-text question answering use separate metrics and scopes. Full-split runs and historical subsets remain separate.

IAM lines / full 2,915-line test

full · cer · Every nonempty transcription in the pinned test split; expected exactly 2,915 lines

Experimental batch-sensitive run — not a reference ranking

Matched singleton checks changed answers materially. Retained for diagnosis; use the full serial reference instead. This run also used a smaller image budget.

OCRBench v1 / full 1,000 questions — experimental batch-4

full · accuracy · All 1,000 questions; question, category, dataset and answer metadata matched against the pinned upstream list

Model / revision	Accuracy ↑	Questions	GPU-slot time	Evidence
Qwen2.5-VL-3B-Instruct66285546d2b8	60.70%	1000	372.2s	Inspect examples →
Qwen2-VL-2B-Instruct895c3a49bc3f	49.00%	1000	268.3s	Inspect examples →

Results by OCRBench task component

Component	Model	Correct / questions	Accuracy
Artistic Text Recognition	Qwen2.5-VL-3B-Instruct	42 / 50	84.0%
Digit String Recognition	Qwen2.5-VL-3B-Instruct	27 / 50	54.0%
Doc-oriented VQA	Qwen2.5-VL-3B-Instruct	109 / 200	54.5%
Handwriting Recognition	Qwen2.5-VL-3B-Instruct	28 / 50	56.0%
Handwritten Mathematical Expression Recognition	Qwen2.5-VL-3B-Instruct	25 / 100	25.0%
Irregular Text Recognition	Qwen2.5-VL-3B-Instruct	39 / 50	78.0%
Key Information Extraction	Qwen2.5-VL-3B-Instruct	119 / 200	59.5%
Non-Semantic Text Recognition	Qwen2.5-VL-3B-Instruct	40 / 50	80.0%
Regular Text Recognition	Qwen2.5-VL-3B-Instruct	43 / 50	86.0%
Scene Text-centric VQA	Qwen2.5-VL-3B-Instruct	135 / 200	67.5%
Artistic Text Recognition	Qwen2-VL-2B-Instruct	31 / 50	62.0%
Digit String Recognition	Qwen2-VL-2B-Instruct	17 / 50	34.0%
Doc-oriented VQA	Qwen2-VL-2B-Instruct	89 / 200	44.5%
Handwriting Recognition	Qwen2-VL-2B-Instruct	27 / 50	54.0%
Handwritten Mathematical Expression Recognition	Qwen2-VL-2B-Instruct	13 / 100	13.0%
Irregular Text Recognition	Qwen2-VL-2B-Instruct	35 / 50	70.0%
Key Information Extraction	Qwen2-VL-2B-Instruct	90 / 200	45.0%
Non-Semantic Text Recognition	Qwen2-VL-2B-Instruct	26 / 50	52.0%
Regular Text Recognition	Qwen2-VL-2B-Instruct	41 / 50	82.0%
Scene Text-centric VQA	Qwen2-VL-2B-Instruct	121 / 200	60.5%

OCRBench v1 / full serial reference

full · accuracy · All 1,000 questions; question, category, dataset and answer metadata matched against the pinned upstream list

GPU-slot time includes model/data loading and CPU scoring. IAM CER is a character-edit ratio. OCRBench accuracy uses upstream answer-in-response matching; it is not document-layout or table quality. Each task page records decoding and image budgets.

02 / Candidate discovery

Open weights first. Evidence alongside.

The legacy snapshot contains 71 models labelled open-weight and 317 reported observations. Use these to build a test shortlist.

Primary metadata refreshed 2026-10-18: 65 repository mappings, 6 software pipelines, 0 unresolved identities. Current repository metadata does not identify the checkpoints used by historical reports. Hub tensor counts may include tied weights.

36 matching model labels · alphabetical, not ranked. Changing the reported-score slice changes observations below; models match the output, deployment and search filters.

Snapshot retrieved 2026-10-18. 93 legacy model labels lack matched model metadata; they appear only when unresolved entries are included. Source dates and import dates remain distinct.

03 / From shortlist to working pipeline

Make the choice practical.

Original OCR archive ↗ · PaddleOCR vs Tesseract · PaddleOCR vs EasyOCR

04 / Coverage, honestly

What we can prove. What comes next.