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 191–200 of 1209 papers

Title	Date	Tasks	Status	Hype
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering	Oct 24, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	1
TLGAN: document Text Localization using Generative Adversarial Nets	Oct 22, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	1
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement	Oct 17, 2020	BinarizationDeblurring	CodeCode Available	1
Tokenization Repair in the Presence of Spelling Errors	Oct 15, 2020	Optical Character Recognition (OCR)Spelling Correction	CodeCode Available	1
Table Structure Recognition using Top-Down and Bottom-Up Cues	Oct 9, 2020	Cell DetectionOptical Character Recognition	CodeCode Available	1
A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes	Oct 1, 2020	Multi-Label ClassificationOptical Character Recognition	CodeCode Available	1
A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching	Sep 17, 2020	Deep LearningEntity Resolution	CodeCode Available	1
Adapting OCR with limited supervision	Jul 27, 2020	Optical Character Recognition (OCR)	CodeCode Available	1
Spatially Aware Multimodal Transformers for TextVQA	Jul 23, 2020	Optical Character Recognition (OCR)Spatial Reasoning	CodeCode Available	1
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated Learning	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