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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1181–1190 of 1209 papers

Title	Date	Tasks	Status
LayoutReader: Pre-training of Text and Layout for Reading Order Detection	Aug 26, 2021	Document Layout AnalysisOptical Character Recognition (OCR)	—Unverified
Learning Adaptive Value of Information for Structured Prediction	Dec 1, 2013	feature selectionModel Selection	—Unverified
Learning Ensembles of Structured Prediction Rules	Jun 1, 2014	Machine TranslationOptical Character Recognition (OCR)	—Unverified
Learning Multiple Tasks in Parallel with a Shared Annotator	Dec 1, 2014	Binary ClassificationDocument Classification	—Unverified
Learning UI Navigation through Demonstrations composed of Macro Actions	Oct 16, 2021	Optical Character Recognition (OCR)	—Unverified
Legal Entity Extraction using a Pointer Generator Network	Jan 20, 2022	named-entity-recognitionNamed Entity Recognition	—Unverified
Lesan -- Machine Translation for Low Resource Languages	Dec 15, 2021	ArticlesMachine Translation	—Unverified
Leveraging Statistical Transliteration for Dictionary-Based English-Bengali CLIR of OCR`d Text	Dec 1, 2012	Information RetrievalOptical Character Recognition (OCR)	—Unverified
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction	Jun 26, 2019	DenoisingOptical Character Recognition (OCR)	—Unverified
License Plate Recognition System Based on Color Coding Of License Plates	Jun 8, 2015	License Plate 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	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