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 501–510 of 1209 papers

Title	Date	Tasks	Status
Auto-ML Deep Learning for Rashi Scripts OCR	Nov 3, 2018	AutoMLDeep Learning	—Unverified
Towards Deployable OCR models for Indic languages	May 13, 2022	Optical Character Recognition (OCR)Segmentation	—Unverified
How many faces can be recognized? Performance extrapolation for multi-class classification	Jun 16, 2016	General ClassificationMulti-class Classification	—Unverified
GMN: Generative Multi-modal Network for Practical Document Information Extraction	Jul 11, 2022	Optical Character Recognition (OCR)	—Unverified
Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images	Feb 20, 2021	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition	Jan 25, 2022	Handwritten Digit RecognitionOptical Character Recognition (OCR)	—Unverified
How Do Large Vision-Language Models See Text in Image? Unveiling the Distinctive Role of OCR Heads	May 21, 2025	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin	Sep 14, 2018	Optical Character Recognition (OCR)	—Unverified
Grouping Language Model Boundary Words to Speed K--Best Extraction from Hypergraphs	Jun 1, 2013	Language ModelingLanguage Modelling	—Unverified
How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR	May 1, 2020	Handwritten Text RecognitionHTR	—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