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 211–220 of 1209 papers

Title	Date	Tasks	Status	Hype
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation	Mar 23, 2020	Domain AdaptationHandwriting generation	CodeCode Available	1
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection	Mar 17, 2020	graph constructionOptical Character Recognition (OCR)	CodeCode Available	1
LAMBERT: Layout-Aware (Language) Modeling for information extraction	Feb 19, 2020	Key Information ExtractionLanguage Modeling	CodeCode Available	1
Image-based table recognition: data, model, and evaluation	Nov 25, 2019	ArticlesDecoder	CodeCode Available	1
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents	May 27, 2019	FormOptical Character Recognition	CodeCode Available	1
Shape Robust Text Detection with Progressive Scale Expansion Network	Mar 28, 2019	Optical Character Recognition (OCR)Scene Text Detection	CodeCode Available	1
Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks	Dec 31, 2018	Handwriting RecognitionLicense Plate Recognition	CodeCode Available	1
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition	Nov 2, 2018	DecoderIrregular Text Recognition	CodeCode Available	1
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector	Feb 26, 2018	Data AugmentationLicense Plate Detection	CodeCode Available	1
EAST: An Efficient and Accurate Scene Text Detector	Apr 11, 2017	Curved Text DetectionOptical Character Recognition (OCR)	CodeCode Available	1

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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