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 151–160 of 1209 papers

Title	Date	Tasks	Status	Hype
Document Dewarping with Control Points	Mar 20, 2022	Optical Character Recognition (OCR)	CodeCode Available	1
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding	Mar 14, 2022	document understandingOptical 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
TableFormer: Table Structure Understanding with Transformers	Mar 2, 2022	Decoderobject-detection	CodeCode Available	1
OCR-IDL: OCR Annotations for Industry Document Library Dataset	Feb 25, 2022	Optical Character Recognition (OCR)	CodeCode Available	1
On the Cross-dataset Generalization in License Plate Recognition	Jan 2, 2022	Data AugmentationLicense Plate Detection	CodeCode Available	1
LaTr: Layout-Aware Transformer for Scene-Text VQA	Dec 23, 2021	Optical Character Recognition (OCR)Question Answering	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
Indian Licence Plate Dataset in the wild	Nov 11, 2021	object-detectionObject Detection	CodeCode Available	1
Lexically Aware Semi-Supervised Learning for OCR Post-Correction	Nov 4, 2021	Language ModellingOptical Character Recognition	CodeCode Available	1

Show:10 25 50

← PrevPage 16 of 121Next →

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