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 421–430 of 1209 papers

Title	Date	Tasks	Status	Score
Combining OCR Models for Reading Early Modern Printed Books	May 11, 2023	Font RecognitionOptical Character Recognition (OCR)	CodeCode Available	5
A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz	Feb 12, 2008	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	5
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images	Jan 26, 2016	DiversityGeneral Classification	CodeCode Available	5
Evaluating Menu OCR and Translation: A Benchmark for Aligning Human and Automated Evaluations in Large Vision-Language Models	Apr 16, 2025	document understandingLayout Design	CodeCode Available	5
FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings	Aug 17, 2023	Image RetrievalLogo Recognition	CodeCode Available	5
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs	Jul 11, 2018	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	5
Quantifying Character Similarity with Vision Transformers	May 24, 2023	Optical Character Recognition (OCR)	CodeCode Available	5
CMUQ@QALB-2014: An SMT-based System for Automatic Arabic Error Correction	Oct 1, 2014	Language ModellingMachine Translation	—Unverified	0
A semi-automatic method for document classification in the shipping industry	Mar 29, 2023	ClassificationDocument Classification	—Unverified	0
Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts	Nov 18, 2020	Clusteringnamed-entity-recognition	—Unverified	0

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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