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 591–600 of 1209 papers

Title	Date	Tasks	Status
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation	Jun 27, 2021	ObjectOptical Character Recognition (OCR)	—Unverified
Modelling Lips-State Detection Using CNN for Non-Verbal Communications	Dec 9, 2021	License Plate DetectionLicense Plate Recognition	—Unverified
A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents	May 4, 2016	ArticlesOptical Character Recognition (OCR)	—Unverified
DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond	Oct 19, 2023	Document AIDocument Layout Analysis	—Unverified
DocVLM: Make Your VLM an Efficient Reader	Dec 11, 2024	document understandingOptical Character Recognition (OCR)	—Unverified
BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition	Feb 18, 2022	License Plate RecognitionManagement	—Unverified
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning	Feb 9, 2023	ObjectOptical Character Recognition (OCR)	—Unverified
Document Layout Analysis via Dynamic Residual Feature Fusion	Apr 7, 2021	Document Layout AnalysisOptical Character Recognition	—Unverified
Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks	Sep 13, 2022	BinarizationOptical Character Recognition (OCR)	—Unverified
Document Enhancement System Using Auto-encoders	Sep 14, 2019	DenoisingDocument Enhancement	—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