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 171–180 of 1209 papers

Title	Date	Tasks	Status	Hype
Indian Licence Plate Dataset in the wild	Nov 11, 2021	object-detectionObject Detection	CodeCode Available	1
Intrinsic Decomposition of Document Images In-the-Wild	Nov 29, 2020	Document Shadow RemovalIntrinsic Image Decomposition	CodeCode Available	1
Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video Environments	Feb 10, 2025	BenchmarkingOptical Character Recognition	CodeCode Available	1
Document Dewarping with Control Points	Mar 20, 2022	Optical Character Recognition (OCR)	CodeCode Available	1
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection	Mar 17, 2020	graph constructionOptical Character Recognition (OCR)	CodeCode Available	1
Data Generation for Post-OCR correction of Cyrillic handwriting	Nov 27, 2023	Handwriting generationHandwritten Text Recognition	CodeCode Available	1
Confidence-aware Non-repetitive Multimodal Transformers for TextCaps	Dec 7, 2020	Image CaptioningOptical Character Recognition	CodeCode Available	1
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks	Sep 20, 2024	AllDependency Parsing	CodeCode Available	1
CORU: Comprehensive Post-OCR Parsing and Receipt Understanding Dataset	Jun 6, 2024	object-detectionObject Detection	CodeCode Available	1
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement	Oct 17, 2020	BinarizationDeblurring	CodeCode Available	1

Show:10 25 50

← PrevPage 18 of 121Next →

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 105M	Accuracy (%)	89.6	—	Unverified
2	DTrOCR	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