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 301–310 of 1209 papers

Title	Date	Tasks	Status
Building A Handwritten Cuneiform Character Imageset	May 1, 2018	Machine TranslationOptical Character Recognition (OCR)	—Unverified
Building a Corpus from Handwritten Picture Postcards: Transcription, Annotation and Part-of-Speech Tagging	May 1, 2018	Optical Character Recognition (OCR)Part-Of-Speech Tagging	—Unverified
DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts	Feb 3, 2022	ArticlesDocument Layout Analysis	—Unverified
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems	Oct 17, 2011	image-classificationImage Classification	—Unverified
BROS: A Pre-trained Language Model for Understanding Texts in Document	Jan 1, 2021	DecoderDiversity	—Unverified
A Novel Machine Learning Based Approach for Post-OCR Error Detection	Sep 1, 2021	BIG-bench Machine LearningOptical Character Recognition	—Unverified
Broken News: Making Newspapers Accessible to Print-Impaired	Jun 21, 2022	Instance SegmentationOptical Character Recognition (OCR)	—Unverified
A Novel Approach to Skew-Detection and Correction of English Alphabets for OCR	Jan 2, 2018	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
A Holistic Approach for Optimizing DSP Block Utilization of a CNN implementation on FPGA	Mar 21, 2017	Optical Character Recognition (OCR)	—Unverified
A Novel Approach to OCR using Image Recognition based Classification for Ancient Tamil Inscriptions in Temples	Jul 4, 2019	BinarizationGeneral Classification	—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