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 221–230 of 1209 papers

Title	Date	Tasks	Status
A semi-automatic method for document classification in the shipping industry	Mar 29, 2023	ClassificationDocument Classification	—Unverified
A second-order orientation-contrast stimulus for population-receptive-field-based retinotopic mapping	Jul 10, 2017	Optical Character Recognition (OCR)	—Unverified
Amazigh Verb Conjugator	May 1, 2014	Optical Character Recognition (OCR)	—Unverified
A Scalable Handwritten Text Recognition System	Apr 19, 2019	Handwriting RecognitionHandwritten Text Recognition	—Unverified
Artificial neural networks and fuzzy logic for recognizing alphabet characters and mathematical symbols	Jul 6, 2016	Image SegmentationOptical Character Recognition	—Unverified
A Maximum Entropy Approach to Chinese Spelling Check	Oct 1, 2013	Optical Character Recognition (OCR)	—Unverified
Context-Independent OCR with Multimodal LLMs: Effects of Image Resolution and Visual Complexity	Mar 31, 2025	Image CaptioningOptical Character Recognition	—Unverified
Artificial Eye for the Blind	Jul 7, 2023	Objectobject-detection	—Unverified
A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction	Sep 6, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Content-based similar document image retrieval using fusion of CNN features	Mar 23, 2017	Image RetrievalOptical 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