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 371–380 of 1209 papers

Title	Date	Tasks	Status
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning	Nov 5, 2024	Continual LearningImitation Learning	—Unverified
HIP: Hierarchical Point Modeling and Pre-training for Visual Information Extraction	Nov 2, 2024	Image ReconstructionOptical Character Recognition (OCR)	—Unverified
Handwriting Recognition in Historical Documents with Multimodal LLM	Oct 31, 2024	Handwriting RecognitionOptical Character Recognition	—Unverified
Structured Analysis and Comparison of Alphabets in Historical Handwritten Ciphers	Oct 29, 2024	CryptanalysisOptical Character Recognition (OCR)	—Unverified
Are VLMs Really Blind	Oct 29, 2024	Language ModelingLanguage Modelling	CodeCode Available
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding	Oct 25, 2024	Benchmarkingdocument understanding	—Unverified
Towards Visual Text Design Transfer Across Languages	Oct 24, 2024	Image GenerationOptical Character Recognition (OCR)	—Unverified
Harnessing Webpage UIs for Text-Rich Visual Understanding	Oct 17, 2024	document understandingOptical Character Recognition (OCR)	—Unverified
Reference-Based Post-OCR Processing with LLM for Diacritic Languages	Oct 17, 2024	Optical Character Recognition (OCR)	—Unverified
LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question Answering	Oct 16, 2024	Optical Character Recognition (OCR)Question Answering	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