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 971–980 of 1209 papers

Title	Date	Tasks	Status
Contrastive Graph Multimodal Model for Text Classification in Videos	Jun 6, 2022	ClassificationContrastive Learning	—Unverified
ConvMath: A Convolutional Sequence Network for Mathematical Expression Recognition	Dec 23, 2020	DecoderOptical Character Recognition	—Unverified
Convolutional Neural Networks for Automatic Meter Reading	Feb 25, 2019	Counter RecognitionData Augmentation	—Unverified
Convolutional Neural Networks for Font Classification	Aug 11, 2017	ClassificationData Augmentation	—Unverified
CorA: A web-based annotation tool for historical and other non-standard language data	Apr 1, 2014	Optical Character Recognition (OCR)	—Unverified
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach	Sep 18, 2019	BIG-bench Machine LearningOptical Character Recognition (OCR)	—Unverified
Corpus of 19th-century Czech Texts: Problems and Solutions	May 1, 2014	Optical Character Recognition (OCR)	—Unverified
Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods	May 1, 2018	ArticlesGrammatical Error Correction	—Unverified
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning	Feb 18, 2025	Optical Character Recognition (OCR)	—Unverified
CREPE: Coordinate-Aware End-to-End Document Parser	May 1, 2024	document understandingOptical 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