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 251–260 of 1209 papers

Title	Date	Tasks	Status
Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts	Nov 18, 2020	Clusteringnamed-entity-recognition	—Unverified
Deep Learning Approach for Receipt Recognition	May 30, 2019	DecoderDeep Learning	—Unverified
Chart-RCNN: Efficient Line Chart Data Extraction from Camera Images	Nov 25, 2022	object-detectionObject Detection	—Unverified
CMUQ@QALB-2014: An SMT-based System for Automatic Arabic Error Correction	Oct 1, 2014	Language ModellingMachine Translation	—Unverified
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter	Jul 4, 2017	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
ChartParser: Automatic Chart Parsing for Print-Impaired	Nov 16, 2022	Optical Character Recognition (OCR)	—Unverified
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering	May 29, 2025	Chart Question AnsweringChart Understanding	—Unverified
A Proposal of Automatic Error Correction in Text	Sep 24, 2021	Information RetrievalLanguage Modelling	—Unverified
A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail	Oct 26, 2022	CPUOptical Character Recognition	—Unverified
ChartEye: A Deep Learning Framework for Chart Information Extraction	Aug 28, 2024	Chart UnderstandingClassification	—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