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 1111–1120 of 1209 papers

Title	Date	Tasks	Status
Resource Constrained Structured Prediction	Feb 28, 2016	Dependency ParsingOptical Character Recognition	—Unverified
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection	Feb 25, 2016	Anomaly DetectionOptical Character Recognition	—Unverified
Improving patch-based scene text script identification with ensembles of conjoined networks	Feb 24, 2016	General ClassificationOptical Character Recognition (OCR)	CodeCode Available
Font Identification in Historical Documents Using Active Learning	Jan 27, 2016	Active LearningDiversity	—Unverified
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images	Jan 26, 2016	DiversityGeneral Classification	CodeCode Available
Decoding Anagrammed Texts Written in an Unknown Language and Script	Jan 1, 2016	DeciphermentLanguage Identification	—Unverified
Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers	Dec 1, 2015	Clusteringnamed-entity-recognition	—Unverified
Comparison of Visual and Logical Character Segmentation in Tesseract OCR Language Data for Indic Writing Scripts	Dec 1, 2015	Optical Character Recognition (OCR)	—Unverified
Calibrated Structured Prediction	Dec 1, 2015	Medical DiagnosisOptical Character Recognition	CodeCode Available
Sequence to Sequence Learning for Optical Character Recognition	Nov 13, 2015	DecoderGeneral Classification	—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