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 641–650 of 1209 papers

Title	Date	Tasks	Status	Hype
Challenging America: Modeling language in longer time scales	Dec 17, 2021	Cloze TestOptical Character Recognition (OCR)	—Unverified	0
Lesan -- Machine Translation for Low Resource Languages	Dec 15, 2021	ArticlesMachine Translation	—Unverified	0
Tracing Text Provenance via Context-Aware Lexical Substitution	Dec 15, 2021	Optical Character Recognition (OCR)Sentence	—Unverified	0
Modelling Lips-State Detection Using CNN for Non-Verbal Communications	Dec 9, 2021	License Plate DetectionLicense Plate Recognition	—Unverified	0
A Survey on Deep learning based Document Image Enhancement	Dec 6, 2021	ArticlesBinarization	—Unverified	0
An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images	Dec 3, 2021	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	1
Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?	Dec 1, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified	0
On-Device Spatial Attention based Sequence Learning Approach for Scene Text Script Identification	Dec 1, 2021	Optical Character Recognition (OCR)	—Unverified	0
OCR-free Document Understanding Transformer	Nov 30, 2021	Document Image Classificationdocument understanding	CodeCode Available	3
Image preprocessing and modified adaptive thresholding for improving OCR	Nov 28, 2021	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified	0

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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