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 821–830 of 1209 papers

Title	Date	Tasks	Status
Language Independent Single Document Image Super-Resolution using CNN for improved recognition	Jan 30, 2017	Image EnhancementImage Super-Resolution	—Unverified
Language Is Not All You Need: Aligning Perception with Language Models	Feb 27, 2023	AllImage Captioning	—Unverified
Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting	Mar 8, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Large Language Models for Page Stream Segmentation	Aug 21, 2024	DecoderOptical Character Recognition	—Unverified
Large Language Models for Simultaneous Named Entity Extraction and Spelling Correction	Mar 1, 2024	DecoderOptical Character Recognition	—Unverified
LayoutReader: Pre-training of Text and Layout for Reading Order Detection	Aug 26, 2021	Document Layout AnalysisOptical Character Recognition (OCR)	—Unverified
Learning Adaptive Value of Information for Structured Prediction	Dec 1, 2013	feature selectionModel Selection	—Unverified
Learning Ensembles of Structured Prediction Rules	Jun 1, 2014	Machine TranslationOptical Character Recognition (OCR)	—Unverified
Learning Multiple Tasks in Parallel with a Shared Annotator	Dec 1, 2014	Binary ClassificationDocument Classification	—Unverified
Learning UI Navigation through Demonstrations composed of Macro Actions	Oct 16, 2021	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