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 1201–1209 of 1209 papers

Title	Date	Tasks	Status
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction	May 8, 2019	image-classificationImage Classification	CodeCode Available
Scene Text Telescope: Text-Focused Scene Image Super-Resolution	Jun 19, 2021	Image Super-ResolutionOptical Character Recognition (OCR)	CodeCode Available
On Manipulating Scene Text in the Wild with Diffusion Models	Nov 1, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
DriveThru: a Document Extraction Platform and Benchmark Datasets for Indonesian Local Language Archives	Nov 14, 2024	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset	Mar 9, 2023	BenchmarkingDeep Learning	CodeCode Available
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images	Jun 26, 2025	document understandingOptical Character Recognition (OCR)	CodeCode Available
SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning	Jun 6, 2023	Caption GenerationImage Captioning	CodeCode Available
Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study	Dec 29, 2024	Motion DetectionOptical Character Recognition	CodeCode Available
Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning	Mar 18, 2024	Handwritten Digit RecognitionOptical Character Recognition	CodeCode Available

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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