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 561–570 of 1209 papers

Title	Date	Tasks	Status
Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach	Mar 8, 2020	Machine TranslationMultimodal Machine Translation	—Unverified
Is it possible to recover personal health information from an automatically de-identified corpus of French EHRs?	Sep 1, 2015	Optical Character Recognition (OCR)	—Unverified
Convolutional Neural Networks for Font Classification	Aug 11, 2017	ClassificationData Augmentation	—Unverified
Convolutional Neural Networks for Automatic Meter Reading	Feb 25, 2019	Counter RecognitionData Augmentation	—Unverified
A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court	May 13, 2025	DiversityDocument Layout Analysis	—Unverified
Improving Long Handwritten Text Line Recognition with Convolutional Multi-way Associative Memory	Nov 5, 2019	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
ConvMath: A Convolutional Sequence Network for Mathematical Expression Recognition	Dec 23, 2020	DecoderOptical Character Recognition	—Unverified
Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principle	Jan 12, 2023	CPUOptical Character Recognition	—Unverified
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model	May 31, 2023	DenoisingOptical Character Recognition (OCR)	—Unverified
Contrastive Graph Multimodal Model for Text Classification in Videos	Jun 6, 2022	ClassificationContrastive Learning	—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 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