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 1111–1120 of 1209 papers

Title	Date	Tasks	Status
From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction	Oct 12, 2019	BIG-bench Machine LearningMachine Translation	CodeCode Available
MRZ code extraction from visa and passport documents using convolutional neural networks	Sep 11, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
Word-Level Alignment of Paper Documents with their Electronic Full-Text Counterparts	Apr 30, 2021	Optical Character Recognition (OCR)	CodeCode Available
ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning	Feb 23, 2020	ArticlesDeep Learning	CodeCode Available
A template-independent approach for information extraction in real estate documents	May 30, 2023	Information RetrievalNatural Language Understanding	CodeCode Available
Multi-Granularity Prediction for Scene Text Recognition	Sep 8, 2022	Language ModelingLanguage Modelling	CodeCode Available
PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Language	May 15, 2025	BenchmarkingOptical Character Recognition	CodeCode Available
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check	Oct 1, 2018	Language ModelingLanguage Modelling	CodeCode Available
State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines	Oct 8, 2018	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs	Jul 11, 2018	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available

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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