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 111–120 of 1209 papers

Title	Date	Tasks	Status	Hype
Southern Newswire Corpus: A Large-Scale Dataset of Mid-Century Wire Articles Beyond the Front Page	Feb 17, 2025	ArticlesOptical Character Recognition (OCR)	—Unverified	0
MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency	Feb 13, 2025	BenchmarkingMath	—Unverified	0
Adapting Multilingual Embedding Models to Historical Luxembourgish	Feb 11, 2025	ArticlesOptical Character Recognition (OCR)	—Unverified	0
Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video Environments	Feb 10, 2025	BenchmarkingOptical Character Recognition	CodeCode Available	1
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents	Feb 6, 2025	Image CaptioningOptical Character Recognition	—Unverified	0
Towards Making Flowchart Images Machine Interpretable	Jan 29, 2025	Code GenerationOptical Character Recognition (OCR)	CodeCode Available	1
MME-Industry: A Cross-Industry Multimodal Evaluation Benchmark	Jan 28, 2025	MMEModel Optimization	—Unverified	0
Ocean-OCR: Towards General OCR Application via a Vision-Language Model	Jan 26, 2025	document understandingLanguage Modeling	CodeCode Available	1
Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical records	Jan 20, 2025	HTROptical Character Recognition (OCR)	CodeCode Available	0
Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images	Jan 16, 2025	De-identificationOptical Character Recognition	—Unverified	0

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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 105M	Accuracy (%)	89.6	—	Unverified
2	DTrOCR	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