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 431–440 of 1209 papers

Title	Date	Tasks	Status
Detection Masking for Improved OCR on Noisy Documents	May 17, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Estimating Post-OCR Denoising Complexity on Numerical Texts	Jul 3, 2023	DenoisingOptical Character Recognition (OCR)	—Unverified
Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections	May 3, 2023	graph constructionOptical Character Recognition	—Unverified
Evaluating Inter-Annotator Agreement on Historical Spelling Normalization	Aug 1, 2016	Optical Character Recognition (OCR)Part-Of-Speech Tagging	—Unverified
CG-DIQA: No-reference Document Image Quality Assessment Based on Character Gradient	Jul 11, 2018	Document Image Quality AssessmentImage Quality Assessment	—Unverified
Evaluating Usage of Images for App Classification	Dec 16, 2019	ClassificationGeneral Classification	—Unverified
Every Pixel Tells a Story: End-to-End Urdu Newspaper OCR	May 20, 2025	ArticlesImage Super-Resolution	—Unverified
Explainable Publication Year Prediction of Eighteenth Century Texts with the BERT Model	May 1, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
TextSleuth: Towards Explainable Tampered Text Detection	Dec 19, 2024	Domain GeneralizationOptical Character Recognition (OCR)	—Unverified
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods	May 31, 2021	ArticlesBIG-bench Machine Learning	—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