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 1131–1140 of 1209 papers

Title	Date	Tasks	Status
A Linked Data Model for Multimodal Sentiment and Emotion Analysis	Jul 1, 2015	Emotion RecognitionOpinion Mining	—Unverified
License Plate Recognition System Based on Color Coding Of License Plates	Jun 8, 2015	License Plate RecognitionOptical Character Recognition	—Unverified
Boosting Optical Character Recognition: A Super-Resolution Approach	Jun 7, 2015	Image Super-ResolutionOptical Character Recognition	—Unverified
Automated Translation of a Literary Work: A Pilot Study	Jun 1, 2015	Machine TranslationOptical Character Recognition (OCR)	—Unverified
Unsupervised Code-Switching for Multilingual Historical Document Transcription	May 1, 2015	Language IdentificationLanguage Modeling	—Unverified
Squibs: Spelling Error Patterns in Brazilian Portuguese	Mar 1, 2015	Information RetrievalOptical Character Recognition (OCR)	—Unverified
Regularization and Kernelization of the Maximin Correlation Approach	Feb 21, 2015	ClassificationGeneral Classification	—Unverified
A survey of modern optical character recognition techniques	Dec 13, 2014	Image EnhancementOptical Character Recognition	—Unverified
A Study of Sindhi Related and Arabic Script Adapted languages Recognition	Dec 13, 2014	ArticlesOptical Character Recognition	—Unverified
Learning Multiple Tasks in Parallel with a Shared Annotator	Dec 1, 2014	Binary ClassificationDocument Classification	—Unverified

Show:10 25 50

← PrevPage 114 of 121Next →

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