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 401–410 of 1209 papers

Title	Date	Tasks	Status
Efficient few-shot learning for pixel-precise handwritten document layout analysis	Oct 27, 2022	Document Layout AnalysisFew-Shot Learning	—Unverified
Efficient, Lexicon-Free OCR using Deep Learning	Jun 5, 2019	Data AugmentationDeep Learning	—Unverified
A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing	Jul 9, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Efficient Media Retrieval from Non-Cooperative Queries	Nov 19, 2014	Optical Character Recognition (OCR)Retrieval	—Unverified
Efficient Medical VIE via Reinforcement Learning	Jun 16, 2025	DiversityOptical Character Recognition (OCR)	—Unverified
Building OCR/NER Test Collections	May 1, 2020	named-entity-recognitionNamed Entity Recognition	—Unverified
Detection Masking for Improved OCR on Noisy Documents	May 17, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods	May 31, 2021	ArticlesBIG-bench Machine Learning	—Unverified
Bypassing Captcha By Machine A Proof For Passing The Turing Test	Sep 3, 2014	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
D\'etection d'erreurs dans des transcriptions OCR de documents historiques par r\'eseaux de neurones r\'ecurrents multi-niveau (Combining character level and word level RNNs for post-OCR error detection)	May 1, 2018	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