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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1201–1209 of 1209 papers

Title	Date	Tasks	Status
Language Classification and Segmentation of Noisy Documents in Hebrew Scripts	Apr 1, 2012	ClassificationGeneral Classification	—Unverified
An Unsupervised and Data-Driven Approach for Spell Checking in Vietnamese OCR-scanned Texts	Apr 1, 2012	Optical Character Recognition (OCR)	—Unverified
Measuring Contextual Fitness Using Error Contexts Extracted from the Wikipedia Revision History	Apr 1, 2012	Optical Character Recognition (OCR)Speech Recognition	—Unverified
Iterative Learning for Reliable Crowdsourcing Systems	Dec 1, 2011	image-classificationImage Classification	—Unverified
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems	Oct 17, 2011	image-classificationImage Classification	—Unverified
From Machine Learning to Machine Reasoning	Feb 9, 2011	BIG-bench Machine LearningLanguage Modeling	—Unverified
An Offline Technique for Localization of License Plates for Indian Commercial Vehicles	Mar 4, 2010	License Plate RecognitionOptical Character Recognition	—Unverified
Adapting the Tesseract Open Source OCR Engine for Multilingual OCR	Jul 25, 2009	Optical Character Recognition (OCR)	CodeCode Available
A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz	Feb 12, 2008	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available

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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