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 291–300 of 1209 papers

Title	Date	Tasks	Status	Score
Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting	Nov 27, 2017	Optical Character Recognition (OCR)	CodeCode Available	5
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing	Jun 1, 2025	Document AIdocument understanding	CodeCode Available	5
Implicit Language Model in LSTM for OCR	May 23, 2018	Language ModelingLanguage Modelling	CodeCode Available	5
A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition	May 7, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	5
Binary Document Image Super Resolution for Improved Readability and OCR Performance	Dec 6, 2018	Image Super-ResolutionInformation Retrieval	CodeCode Available	5
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning	Feb 27, 2018	Active LearningOptical Character Recognition (OCR)	CodeCode Available	5
InstructOCR: Instruction Boosting Scene Text Spotting	Dec 20, 2024	Optical Character Recognition (OCR)Text Spotting	CodeCode Available	5
An Evaluation of OCR on Egocentric Data	Jun 11, 2022	Optical Character Recognition (OCR)	CodeCode Available	5
BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata Extraction	Mar 25, 2025	document understandingobject-detection	CodeCode Available	5
iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition	Jun 27, 2022	Face DetectionFace Recognition	CodeCode Available	5

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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