Optical Character Recognition (OCR)

Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)

Papers

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Showing 21–30 of 1209 papers

Title	Date	Tasks	Status	Hype
MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities	Aug 1, 2024	MathMM-Vet	CodeCode Available	3
Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models	Dec 11, 2023	Chart UnderstandingDecoder	CodeCode Available	3
OCR-free Document Understanding Transformer	Nov 30, 2021	Document Image Classificationdocument understanding	CodeCode Available	3
Image-to-Markup Generation with Coarse-to-Fine Attention	Sep 16, 2016	DecoderOptical Character Recognition (OCR)	CodeCode Available	3
Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration	Jul 7, 2025	Optical Character Recognition (OCR)	CodeCode Available	2
Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability	Jun 10, 2025	Optical Character Recognition (OCR)	CodeCode Available	2
MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories	Jun 5, 2025	BenchmarkingOptical Character Recognition	CodeCode Available	2
An Approach for Air Drawing Using Background Subtraction and Contour Extraction	Mar 3, 2025	Hand DetectionOptical Character Recognition (OCR)	CodeCode Available	2
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining	Jan 1, 2025	Optical Character Recognition (OCR)	CodeCode Available	2
TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action	Dec 7, 2024	Depth EstimationMathematical Reasoning	CodeCode Available	2

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All datasets Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study VideoDB's OCR Benchmark Public Collection FSNS - Test I2L-140K SUT im2latex-100k

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DTrOCR	Accuracy (%)	89.6	—	Unverified
2	DTrOCR 105M	Accuracy (%)	89.6	—	Unverified
3	MaskOCR-L	Accuracy (%)	82.6	—	Unverified
4	TransOCR	Accuracy (%)	72.8	—	Unverified
5	SRN	Accuracy (%)	65	—	Unverified
6	MORAN	Accuracy (%)	64.3	—	Unverified
7	SEED	Accuracy (%)	61.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GPT-4o	Average Accuracy	76.22	—	Unverified
2	Gemini-1.5 Pro	Average Accuracy	76.13	—	Unverified
3	Claude-3 Sonnet	Average Accuracy	67.71	—	Unverified
4	RapidOCR	Average Accuracy	56.98	—	Unverified
5	EasyOCR	Average Accuracy	49.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	STREET	Sequence error	27.54	—	Unverified
2	SEE	Sequence error	22	—	Unverified
3	AttentionOCR_Inception-resnet-v2_Location	Sequence error	15.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	I2L-NOPOOL	BLEU	89.09	—	Unverified
2	I2L-STRIPS	BLEU	89	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Tesseract	Character Error Rate (CER)	0.08	—	Unverified
2	EasyOCR	Character Error Rate (CER)	0.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	I2L-STRIPS	BLEU	88.86	—	Unverified