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 321–330 of 1209 papers

Title	Date	Tasks	Status
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents	Feb 6, 2025	Image CaptioningOptical Character Recognition	—Unverified
End-to-End Piece-Wise Unwarping of Document Images	Jan 1, 2021	MS-SSIMOptical Character Recognition (OCR)	—Unverified
Boosting Optical Character Recognition: A Super-Resolution Approach	Jun 7, 2015	Image Super-ResolutionOptical Character Recognition	—Unverified
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution	Oct 18, 2020	Face DetectionOptical Character Recognition	—Unverified
An Offline Technique for Localization of License Plates for Indian Commercial Vehicles	Mar 4, 2010	License Plate RecognitionOptical Character Recognition	—Unverified
Books of Hours. the First Liturgical Data Set for Text Segmentation.	May 1, 2020	Handwritten Text RecognitionHTR	—Unverified
Modelling Lips-State Detection Using CNN for Non-Verbal Communications	Dec 9, 2021	License Plate DetectionLicense Plate Recognition	—Unverified
A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents	May 4, 2016	ArticlesOptical Character Recognition (OCR)	—Unverified
A Comparative Study of Filtering Approaches Applied to Color Archival Document Images	Aug 16, 2019	DenoisingImage Enhancement	—Unverified
BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition	Feb 18, 2022	License Plate RecognitionManagement	—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