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 851–860 of 1209 papers

Title	Date	Tasks	Status
A Hybrid Swarm and Gravitation based feature selection algorithm for Handwritten Indic Script Classification problem	May 10, 2020	Classificationfeature selection	—Unverified
Development of a New Image-to-text Conversion System for Pashto, Farsi and Traditional Chinese	May 8, 2020	Image to textOptical Character Recognition (OCR)	—Unverified
A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition	May 7, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation	May 1, 2020	Machine TranslationNMT	—Unverified
How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR	May 1, 2020	Handwritten Text RecognitionHTR	—Unverified
Books of Hours. the First Liturgical Data Set for Text Segmentation.	May 1, 2020	Handwritten Text RecognitionHTR	—Unverified
Preserving Semantic Information from Old Dictionaries: Linking Senses of the `Altfranz\"osisches W\"orterbuch' to WordNet	May 1, 2020	Optical Character Recognition (OCR)	—Unverified
Constructing a Public Meeting Corpus	May 1, 2020	Optical Character Recognition (OCR)	—Unverified
Time-Aware Word Embeddings for Three Lebanese News Archives	May 1, 2020	Optical Character Recognition (OCR)Word Embeddings	CodeCode Available
Building OCR/NER Test Collections	May 1, 2020	named-entity-recognitionNamed Entity Recognition	—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	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