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 621–630 of 1209 papers

Title	Date	Tasks	Status
ChartParser: Automatic Chart Parsing for Print-Impaired	Nov 16, 2022	Optical Character Recognition (OCR)	—Unverified
Efficient few-shot learning for pixel-precise handwritten document layout analysis	Oct 27, 2022	Document Layout AnalysisFew-Shot Learning	—Unverified
A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail	Oct 26, 2022	CPUOptical Character Recognition	—Unverified
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model	Oct 15, 2022	Food recommendationLearning-To-Rank	—Unverified
Key Information Extraction in Purchase Documents using Deep Learning and Rule-based Corrections	Oct 7, 2022	Key Information ExtractionLine Detection	—Unverified
EraseNet: A Recurrent Residual Network for Supervised Document Cleaning	Oct 3, 2022	DenoisingOptical Character Recognition	—Unverified
Chandojnanam: A Sanskrit Meter Identification and Utilization System	Sep 29, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
Synthesizing Annotated Image and Video Data Using a Rendering-Based Pipeline for Improved License Plate Recognition	Sep 28, 2022	Data AugmentationLicense Plate Recognition	—Unverified
3D Rendering Framework for Data Augmentation in Optical Character Recognition	Sep 27, 2022	Data AugmentationOptical Character Recognition	—Unverified
Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering	Sep 21, 2022	Image CaptioningOptical 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