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
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers	May 27, 2023	Image Super-ResolutionLicense Plate Recognition	CodeCode Available	1
Exploring Better Text Image Translation with Multimodal Codebook	May 27, 2023	Machine TranslationOptical Character Recognition	CodeCode Available	1
MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition	May 24, 2023	Continual LearningIncremental Learning	CodeCode Available	1
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages	May 19, 2023	In-Context LearningMultilingual NLP	CodeCode Available	1
Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution	May 12, 2023	Contrastive LearningOptical Character Recognition (OCR)	CodeCode Available	1
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition	May 9, 2023	Optical Character Recognition (OCR)Scene Text Recognition	CodeCode Available	1
DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents	Apr 24, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	1
TagGPT: Large Language Models are Zero-shot Multimodal Taggers	Apr 6, 2023	Optical Character Recognition (OCR)Prompt Engineering	CodeCode Available	1
ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules	Apr 5, 2023	Chart UnderstandingDerendering	CodeCode Available	1
Efficient OCR for Building a Diverse Digital History	Apr 5, 2023	DiversityImage Retrieval	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