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 1091–1100 of 1209 papers

Title	Date	Tasks	Status
Semi-automated annotation of page-based documents within the Genre and Multimodality framework	Aug 1, 2016	Optical Character Recognition (OCR)	—Unverified
Unsupervised morph segmentation and statistical language models for vocabulary expansion	Aug 1, 2016	Automatic Speech Recognition (ASR)Language Modeling	—Unverified
Benchmark for License Plate Character Segmentation	Jul 11, 2016	License Plate DetectionLicense Plate Recognition	—Unverified
Artificial neural networks and fuzzy logic for recognizing alphabet characters and mathematical symbols	Jul 6, 2016	Image SegmentationOptical Character Recognition	—Unverified
How many faces can be recognized? Performance extrapolation for multi-class classification	Jun 16, 2016	General ClassificationMulti-class Classification	—Unverified
An Unsupervised Model of Orthographic Variation for Historical Document Transcription	Jun 1, 2016	Optical Character Recognition (OCR)	CodeCode Available
An efficient way for segmentation of Bangla characters in printed document using curved scanning	May 13, 2016	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents	May 4, 2016	ArticlesOptical Character Recognition (OCR)	—Unverified
Using SMT for OCR Error Correction of Historical Texts	May 1, 2016	ArticlesLanguage Modelling	—Unverified
OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles	May 1, 2016	Optical 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	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