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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 191–200 of 1209 papers

Title	Date	Tasks	Status	Hype	Score
A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching	Sep 17, 2020	Deep LearningEntity Resolution	CodeCode Available	1	5
Image-based table recognition: data, model, and evaluation	Nov 25, 2019	ArticlesDecoder	CodeCode Available	1	5
FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts	Nov 9, 2023	Optical Character Recognition (OCR)Safety Alignment	CodeCode Available	1	5
From Text to Pixel: Advancing Long-Context Understanding in MLLMs	May 23, 2024	Language ModelingLanguage Modelling	CodeCode Available	1	5
Exploring Cross-Image Pixel Contrast for Semantic Segmentation	Jan 28, 2021	Metric LearningOptical Character Recognition (OCR)	CodeCode Available	1	5
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents	Aug 10, 2021	Key Information ExtractionLanguage Modeling	CodeCode Available	1	5
Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation	Oct 25, 2023	Handwritten Text RecognitionKey Information Extraction	CodeCode Available	1	5
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition	Nov 2, 2018	DecoderIrregular Text Recognition	CodeCode Available	1	5
Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter	Jun 10, 2021	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	1	5
LAMBERT: Layout-Aware (Language) Modeling for information extraction	Feb 19, 2020	Key Information ExtractionLanguage Modeling	CodeCode Available	1	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