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 631–640 of 1209 papers

Title	Date	Tasks	Status
Slide2Text: Leveraging LLMs for Personalized Textbook Generation from PowerPoint Presentations	Mar 22, 2025	Optical Character Recognition (OCR)	—Unverified
Solution for SMART-101 Challenge of ICCV Multi-modal Algorithmic Reasoning Task 2023	Oct 10, 2023	Decoderobject-detection	—Unverified
Solving Substitution Ciphers with Combined Language Models	Aug 1, 2014	Machine TranslationOptical Character Recognition (OCR)	—Unverified
Southern Newswire Corpus: A Large-Scale Dataset of Mid-Century Wire Articles Beyond the Front Page	Feb 17, 2025	ArticlesOptical Character Recognition (OCR)	—Unverified
SPARLING: Learning Latent Representations with Extremely Sparse Activations	Feb 3, 2023	Optical Character Recognition (OCR)	—Unverified
Sparse Concept Coded Tetrolet Transform for Unconstrained Odia Character Recognition	Apr 3, 2020	Optical Character Recognition (OCR)	—Unverified
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check	Nov 1, 2021	Graph Neural NetworkLanguage Modeling	—Unverified
Squibs: Spelling Error Patterns in Brazilian Portuguese	Mar 1, 2015	Information RetrievalOptical Character Recognition (OCR)	—Unverified
Star-net: A spatial attention residue network for scene text recognition.	Sep 20, 2016	Optical Character Recognition (OCR)Scene Text Recognition	—Unverified
Statistical Learning for OCR Text Correction	Nov 21, 2016	Optical Character RecognitionOptical 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 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