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 961–970 of 1209 papers

Title	Date	Tasks	Status
Image-based Natural Language Understanding Using 2D Convolutional Neural Networks	Oct 24, 2018	ClassificationGeneral Classification	—Unverified
State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines	Oct 8, 2018	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features	Oct 1, 2018	AttributeGeneralized Zero-Shot Learning	—Unverified
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check	Oct 1, 2018	Language ModelingLanguage Modelling	CodeCode Available
Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin	Sep 14, 2018	Optical Character Recognition (OCR)	—Unverified
Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit	Sep 6, 2018	Optical Character Recognition (OCR)	CodeCode Available
License Plate Detection and Recognition in Unconstrained Scenarios	Sep 1, 2018	License Plate DetectionLicense Plate Recognition	CodeCode Available
Indigenous language technologies in Canada: Assessment, challenges, and successes	Aug 1, 2018	Machine TranslationOptical Character Recognition	—Unverified
Local String Transduction as Sequence Labeling	Aug 1, 2018	LemmatizationMachine Translation	—Unverified
Towards an Automatic Classification of Illustrative Examples in a Large Japanese-French Dictionary Obtained by OCR	Aug 1, 2018	General ClassificationMachine Translation	—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