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 1031–1040 of 1209 papers

Title	Date	Tasks	Status
LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images	Dec 16, 2022	DecoderOptical Character Recognition (OCR)	CodeCode Available
Order-preserving Consistency Regularization for Domain Adaptation and Generalization	Sep 23, 2023	Data AugmentationDomain Adaptation	CodeCode Available
LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question Answering	Oct 16, 2024	Optical Character Recognition (OCR)Question Answering	CodeCode Available
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks	Nov 13, 2019	Fine-Grained Vehicle ClassificationLicense Plate Detection	CodeCode Available
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts	Dec 15, 2019	DiversityInstance Segmentation	CodeCode Available
Improving patch-based scene text script identification with ensembles of conjoined networks	Feb 24, 2016	General ClassificationOptical Character Recognition (OCR)	CodeCode Available
Levenshtein OCR	Sep 8, 2022	Imitation LearningOptical Character Recognition (OCR)	CodeCode Available
ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in Images	Apr 16, 2024	Multimodal Deep LearningOptical Character Recognition (OCR)	CodeCode Available
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding	Dec 19, 2022	Contrastive Learningdocument understanding	CodeCode Available
Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting	Nov 27, 2017	Optical Character Recognition (OCR)	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