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 911–920 of 1209 papers

Title	Date	Tasks	Status
Nonparametric modeling cash flows of insurance company	Aug 14, 2019	Optical Character Recognition (OCR)	—Unverified
Self-supervised Data Bootstrapping for Deep Optical Character Recognition of Identity Documents	Aug 12, 2019	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Mitigating Noisy Inputs for Question Answering	Aug 8, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Answering Questions about Data Visualizations using Efficient Bimodal Fusion	Aug 5, 2019	Chart Question AnsweringOptical Character Recognition	CodeCode Available
OCR Quality and NLP Preprocessing	Aug 1, 2019	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Ekush: A Multipurpose and Multitype Comprehensive Database for Online Off-Line Bangla Handwritten Characters	Jul 17, 2019	Optical Character Recognition (OCR)	CodeCode Available
Multimodal deep networks for text and image-based document classification	Jul 15, 2019	ClassificationDocument Classification	CodeCode Available
A Novel Approach to OCR using Image Recognition based Classification for Ancient Tamil Inscriptions in Temples	Jul 4, 2019	BinarizationGeneral Classification	—Unverified
Brno Mobile OCR Dataset	Jul 2, 2019	BinarizationDenoising	CodeCode Available
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction	Jun 26, 2019	DenoisingOptical Character Recognition (OCR)	—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