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 321–330 of 1209 papers

Title	Date	Tasks	Status	Score
DeQA-Doc: Adapting DeQA-Score to Document Image Quality Assessment	Jul 17, 2025	Document Image Quality AssessmentImage Quality Assessment	CodeCode Available	5
DELINE8K: A Synthetic Data Pipeline for the Semantic Segmentation of Historical Documents	Apr 30, 2024	8kDiversity	CodeCode Available	5
An efficient way for segmentation of Bangla characters in printed document using curved scanning	May 13, 2016	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	5
Automatic Recognition of Learning Resource Category in a Digital Library	Nov 28, 2023	document-image-classificationDocument Image Classification	CodeCode Available	5
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations	Jul 1, 2021	Key Information ExtractionOptical Character Recognition (OCR)	CodeCode Available	5
HENet: Forcing a Network to Think More for Font Recognition	Oct 21, 2021	Font RecognitionOptical Character Recognition (OCR)	CodeCode Available	5
Handwritten Code Recognition for Pen-and-Paper CS Education	Aug 7, 2024	HallucinationLanguage Modeling	CodeCode Available	5
Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning	Mar 18, 2024	Handwritten Digit RecognitionOptical Character Recognition	CodeCode Available	5
Handwriting Classification for the Analysis of Art-Historical Documents	Nov 4, 2020	ClassificationGeneral Classification	CodeCode Available	5
Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Network	Jun 12, 2019	Optical Character Recognition (OCR)Text Segmentation	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 105M	Accuracy (%)	89.6	—	Unverified
2	DTrOCR	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