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 721–730 of 1209 papers

Title	Date	Tasks	Status
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check	Nov 1, 2021	Graph Neural NetworkLanguage Modeling	—Unverified
Unsupervised Multi-View Post-OCR Error Correction With Language Models	Nov 1, 2021	Domain AdaptationLanguage Modeling	—Unverified
Named Entity Recognition in Historic Legal Text: A Transformer and State Machine Ensemble Method	Nov 1, 2021	Language ModelingLanguage Modelling	—Unverified
BART for Post-Correction of OCR Newspaper Text	Nov 1, 2021	Language ModelingLanguage Modelling	—Unverified
Ultra Light OCR Competition Technical Report	Oct 25, 2021	Optical Character Recognition (OCR)Scene Text Recognition	—Unverified
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts	Oct 22, 2021	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
HENet: Forcing a Network to Think More for Font Recognition	Oct 21, 2021	Font RecognitionOptical Character Recognition (OCR)	CodeCode Available
Learning UI Navigation through Demonstrations composed of Macro Actions	Oct 16, 2021	Optical Character Recognition (OCR)	—Unverified
Optical Character Recognition of 19th Century Classical Commentaries: the Current State of Affairs	Oct 13, 2021	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks	Oct 8, 2021	FormOptical 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	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