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 121–130 of 1209 papers

Title	Date	Tasks	Status	Hype
DocScanner: Robust Document Image Rectification with Progressive Learning	Oct 28, 2021	Optical Character Recognition (OCR)	CodeCode Available	1
Document Dewarping with Control Points	Mar 20, 2022	Optical Character Recognition (OCR)	CodeCode Available	1
End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-Net	Jun 2, 2021	Optical Character Recognition (OCR)	CodeCode Available	1
FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions	May 28, 2023	AttributeImage Captioning	CodeCode Available	1
DocFormerv2: Local Features for Document Understanding	Jun 2, 2023	Decoderdocument understanding	CodeCode Available	1
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders	Jun 10, 2020	Cell SegmentationDenoising	CodeCode Available	1
DocLayLLM: An Efficient and Effective Multi-modal Extension of Large Language Models for Text-rich Document Understanding	Aug 27, 2024	document understandingOptical Character Recognition (OCR)	CodeCode Available	1
Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks	Dec 31, 2018	Handwriting RecognitionLicense Plate Recognition	CodeCode Available	1
An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images	Dec 3, 2021	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	1
DiT: Self-supervised Pre-training for Document Image Transformer	Mar 4, 2022	Document AIdocument-image-classification	CodeCode Available	1

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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