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 391–400 of 1209 papers

Title	Date	Tasks	Status	Score
From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction	Oct 12, 2019	BIG-bench Machine LearningMachine Translation	CodeCode Available	5
Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical records	Jan 20, 2025	HTROptical Character Recognition (OCR)	CodeCode Available	5
Gated Recurrent Convolution Neural Network for OCR	Dec 1, 2017	General Classificationimage-classification	CodeCode Available	5
A Multi-Object Rectified Attention Network for Scene Text Recognition	Jan 10, 2019	DecoderObject	CodeCode Available	5
Handwriting Classification for the Analysis of Art-Historical Documents	Nov 4, 2020	ClassificationGeneral Classification	CodeCode Available	5
EATEN: Entity-aware Attention for Single Shot Visual Text Extraction	Sep 20, 2019	DecoderEntity Extraction using GAN	CodeCode Available	5
Quantifying Character Similarity with Vision Transformers	May 24, 2023	Optical Character Recognition (OCR)	CodeCode Available	5
FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text Spotting	Aug 27, 2024	BenchmarkingDecoder	CodeCode Available	5
FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings	Aug 17, 2023	Image RetrievalLogo Recognition	CodeCode Available	5
A Survey of Deep Learning Approaches for OCR and Document Understanding	Nov 27, 2020	document understandingOptical Character Recognition (OCR)	CodeCode Available	5

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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