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 961–970 of 1209 papers

Title	Date	Tasks	Status
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation	Nov 22, 2022	Optical Character Recognition (OCR)Semantic Segmentation	—Unverified
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning	Nov 5, 2024	Continual LearningImitation Learning	—Unverified
Out-of-Vocabulary Challenge Report	Sep 14, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Overlay Text Extraction From TV News Broadcast	Apr 2, 2016	ArticlesOptical Character Recognition (OCR)	—Unverified
Overview of the 2017 ALTA Shared Task: Correcting OCR Errors	Dec 1, 2017	Optical Character Recognition (OCR)	—Unverified
PACMAN: a framework for pulse oximeter digit detection and reading in a low-resource setting	Dec 9, 2022	object-detectionObject Detection	—Unverified
PaddleOCR 3.0 Technical Report	Jul 8, 2025	document understandingKey Information Extraction	—Unverified
Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features	Oct 9, 2017	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
PAM: Understanding Product Images in Cross Product Category Attribute Extraction	Jun 8, 2021	AttributeAttribute Extraction	—Unverified
papago: A Machine Translation Service with Word Sense Disambiguation and Currency Conversion	Dec 1, 2016	Machine TranslationOptical 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