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 991–1000 of 1209 papers

Title	Date	Tasks	Status
Providing and Analyzing NLP Terms for our Community	Dec 1, 2016	Named Entity Recognition (NER)Optical Character Recognition (OCR)	—Unverified
Pseudo-Bidirectional Decoding for Local Sequence Transduction	Jan 31, 2020	DecoderGrammatical Error Correction	—Unverified
Pynini: A Python library for weighted finite-state grammar compilation	Aug 1, 2016	Automatic Speech Recognition (ASR)Optical Character Recognition (OCR)	—Unverified
Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition	Jul 18, 2024	DecoderHandwriting Recognition	—Unverified
QID: Efficient Query-Informed ViTs in Data-Scarce Regimes for OCR-free Visual Document Understanding	Apr 3, 2025	document understandingLanguage Modeling	—Unverified
Quantitative Analysis of Image Classification Techniques for Memory-Constrained Devices	May 11, 2020	ClassificationGeneral Classification	—Unverified
RDU: A Region-based Approach to Form-style Document Understanding	Jun 14, 2022	document understandingForm	—Unverified
Reading Ancient Coin Legends: Object Recognition vs. OCR	Apr 26, 2013	ObjectObject Recognition	—Unverified
Reading in the Dark with Foveated Event Vision	Jun 7, 2025	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models	May 28, 2024	Document EnhancementMixed Reality	—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	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