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 811–820 of 1209 papers

Title	Date	Tasks	Status
A Panoramic Survey of Natural Language Processing in the Arab World	Nov 25, 2020	Machine TranslationOptical Character Recognition	—Unverified
SuperOCR: A Conversion from Optical Character Recognition to Image Captioning	Nov 21, 2020	Image CaptioningLicense Plate Recognition	—Unverified
On-Device Text Image Super Resolution	Nov 20, 2020	Cloud ComputingGPU	—Unverified
Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts	Nov 18, 2020	Clusteringnamed-entity-recognition	—Unverified
On-Device Language Identification of Text in Images using Diacritic Characters	Nov 10, 2020	Language Identificationobject-detection	—Unverified
Automated data extraction of bar chart raster images	Nov 9, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Handwriting Classification for the Analysis of Art-Historical Documents	Nov 4, 2020	ClassificationGeneral Classification	CodeCode Available
Automated Transcription of Non-Latin Script Periodicals: A Case Study in the Ottoman Turkish Print Archive	Nov 2, 2020	HTROptical Character Recognition (OCR)	—Unverified
OCR, Classification & Machine Translation (OCCAM)	Nov 1, 2020	Classificationimage-classification	—Unverified
Chunk-based Chinese Spelling Check with Global Optimization	Nov 1, 2020	global-optimizationOptical Character Recognition	—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