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 571–580 of 1209 papers

Title	Date	Tasks	Status
Contrastive Graph Multimodal Model for Text Classification in Videos	Jun 6, 2022	ClassificationContrastive Learning	—Unverified
A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court	May 13, 2025	DiversityDocument Layout Analysis	—Unverified
Improving OCR-Based Image Captioning by Incorporating Geometrical Relationship	Jun 19, 2021	Image CaptioningOptical Character Recognition (OCR)	—Unverified
Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach	Jan 15, 2024	Optical Character Recognition (OCR)	—Unverified
1 Million Captioned Dutch Newspaper Images	May 1, 2016	ArticlesData-to-Text Generation	—Unverified
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding	Nov 12, 2024	document understandingOptical Character Recognition (OCR)	—Unverified
Improving Text Generation on Images with Synthetic Captions	Jun 1, 2024	Optical Character Recognition (OCR)Text Generation	—Unverified
IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection	May 3, 2018	Multi-Oriented Scene Text Detectionobject-detection	—Unverified
Improving Document Clustering by Removing Unnatural Language	Sep 1, 2017	ClusteringDocument Layout Analysis	—Unverified
Improving Amharic Handwritten Word Recognition Using Auxiliary Task	Feb 25, 2022	Handwritten Text RecognitionOptical Character Recognition	—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