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 591–600 of 1209 papers

Title	Date	Tasks	Status
CLIP-ReIdent: Contrastive Training for Player Re-Identification	Mar 21, 2023	Optical Character Recognition (OCR)Sports Analytics	—Unverified
Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh	Mar 21, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
The System Description of dun_oscar team for The ICPR MSR Challenge	Mar 13, 2023	Optical Character Recognition (OCR)	—Unverified
BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset	Mar 9, 2023	BenchmarkingDeep Learning	CodeCode Available
Meme Sentiment Analysis Enhanced with Multimodal Spatial Encoding and Facial Embedding	Mar 3, 2023	Optical Character Recognition (OCR)Position	—Unverified
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training	Mar 1, 2023	Document Image Classificationimage-classification	CodeCode Available
Language Is Not All You Need: Aligning Perception with Language Models	Feb 27, 2023	AllImage Captioning	—Unverified
User-Centric Evaluation of OCR Systems for Kwak'wala	Feb 26, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning	Feb 9, 2023	ObjectOptical Character Recognition (OCR)	—Unverified
SPARLING: Learning Latent Representations with Extremely Sparse Activations	Feb 3, 2023	Optical Character Recognition (OCR)	—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