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 561–570 of 1209 papers

Title	Date	Tasks	Status
Estimating Post-OCR Denoising Complexity on Numerical Texts	Jul 3, 2023	DenoisingOptical Character Recognition (OCR)	—Unverified
Fraunhofer SIT at CheckThat! 2023: Mixing Single-Modal Classifiers to Estimate the Check-Worthiness of Multi-Modal Tweets	Jul 2, 2023	Fact CheckingOptical Character Recognition (OCR)	—Unverified
Resume Information Extraction via Post-OCR Text Processing	Jun 23, 2023	Object RecognitionOptical Character Recognition	—Unverified
A Survey on Multimodal Large Language Models	Jun 23, 2023	HallucinationIn-Context Learning	—Unverified
Document Image Cleaning using Budget-Aware Black-Box Approximation	Jun 22, 2023	Optical Character Recognition (OCR)	CodeCode Available
When Vision Fails: Text Attacks Against ViT and OCR	Jun 12, 2023	Optical Character Recognition (OCR)	CodeCode Available
Weakly supervised information extraction from inscrutable handwritten document images	Jun 12, 2023	Language ModelingLanguage Modelling	—Unverified
SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning	Jun 6, 2023	Caption GenerationImage Captioning	CodeCode Available
Transformer-Based UNet with Multi-Headed Cross-Attention Skip Connections to Eliminate Artifacts in Scanned Documents	Jun 5, 2023	DenoisingDocument Classification	—Unverified
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model	May 31, 2023	DenoisingOptical 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