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 241–250 of 1209 papers

Title	Date	Tasks	Status
The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing	Jun 7, 2025	Meta-LearningModel Editing	—Unverified
Reading in the Dark with Foveated Event Vision	Jun 7, 2025	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
A Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions	Jun 5, 2025	Computational Efficiencydocument understanding	—Unverified
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing	Jun 1, 2025	Document AIdocument understanding	CodeCode Available
Predicting the Past: Estimating Historical Appraisals with OCR and Machine Learning	May 30, 2025	Optical Character Recognition (OCR)	CodeCode Available
SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition	May 30, 2025	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Synthetic Document Question Answering in Hungarian	May 29, 2025	Optical Character Recognition (OCR)Question Answering	CodeCode Available
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering	May 29, 2025	Chart Question AnsweringChart Understanding	—Unverified
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance	May 29, 2025	Image Super-ResolutionOptical Character Recognition	—Unverified
E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing	May 27, 2025	Decision MakingOptical Character Recognition (OCR)	—Unverified

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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