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 581–590 of 1209 papers

Title	Date	Tasks	Status
An Evaluation of OCR on Egocentric Data	Jun 11, 2022	Optical Character Recognition (OCR)	CodeCode Available
Transformer based Urdu Handwritten Text Optical Character Reader	Jun 9, 2022	Natural Language UnderstandingOptical Character Recognition (OCR)	—Unverified
PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System	Jun 7, 2022	Data AugmentationOptical Character Recognition	—Unverified
Contrastive Graph Multimodal Model for Text Classification in Videos	Jun 6, 2022	ClassificationContrastive Learning	—Unverified
Two Decades of Bengali Handwritten Digit Recognition: A Survey	Jun 5, 2022	Handwritten Digit RecognitionOptical Character Recognition	—Unverified
Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks	Jun 2, 2022	ClassificationOptical Character Recognition	—Unverified
Generating Monolingual Dataset for Low Resource Language Bodo from old books using Google Keep	Jun 1, 2022	Optical Character Recognition (OCR)	—Unverified
Multilingual Named Entity Recognition for Medieval Charters Using Stacked Embeddings and Bert-based Models.	Jun 1, 2022	HTRMultilingual Named Entity Recognition	—Unverified
A Language Modelling Approach to Quality Assessment of OCR’ed Historical Text	Jun 1, 2022	Language ModelingLanguage Modelling	—Unverified
An Open Source Contractual Language Understanding Application Using Machine Learning	Jun 1, 2022	Document Text ClassificationInformation Retrieval	CodeCode Available

Show:10 25 50

← PrevPage 59 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