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 551–560 of 1209 papers

Title	Date	Tasks	Status
Optimizing the Neural Network Training for OCR Error Correction of Historical Hebrew Texts	Jul 30, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Augmented Math: Authoring AR-Based Explorable Explanations by Augmenting Static Math Textbooks	Jul 30, 2023	MathOptical Character Recognition	CodeCode Available
Multi-Granularity Prediction with Learnable Fusion for Scene Text Recognition	Jul 25, 2023	Language ModellingOptical Character Recognition (OCR)	—Unverified
MataDoc: Margin and Text Aware Document Dewarping for Arbitrary Boundary	Jul 24, 2023	document understandingOptical Character Recognition (OCR)	—Unverified
A comparative analysis of SRGAN models	Jul 18, 2023	Generative Adversarial NetworkImage Super-Resolution	—Unverified
Handwritten and Printed Text Segmentation: A Signature Case Study	Jul 15, 2023	Binary ClassificationOptical Character Recognition	—Unverified
Handwritten Text Recognition Using Convolutional Neural Network	Jul 11, 2023	Handwritten Text RecognitionOptical Character Recognition	—Unverified
A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing	Jul 9, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Artificial Eye for the Blind	Jul 7, 2023	Objectobject-detection	—Unverified
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding	Jul 4, 2023	document understandingLanguage Modeling	—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