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 581–590 of 1209 papers

Title	Date	Tasks	Status
A Holistic Approach for Optimizing DSP Block Utilization of a CNN implementation on FPGA	Mar 21, 2017	Optical Character Recognition (OCR)	—Unverified
A Conglomerate of Multiple OCR Table Detection and Extraction	Oct 16, 2020	Optical Character Recognition (OCR)Table Detection	—Unverified
3D Rendering Framework for Data Augmentation in Optical Character Recognition	Sep 27, 2022	Data AugmentationOptical Character Recognition	—Unverified
Boosting Optical Character Recognition: A Super-Resolution Approach	Jun 7, 2015	Image Super-ResolutionOptical Character Recognition	—Unverified
DOTA: Deformable Optimized Transformer Architecture for End-to-End Text Recognition with Retrieval-Augmented Generation	May 7, 2025	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution	Oct 18, 2020	Face DetectionOptical Character Recognition	—Unverified
An Offline Technique for Localization of License Plates for Indian Commercial Vehicles	Mar 4, 2010	License Plate RecognitionOptical Character Recognition	—Unverified
DoPTA: Improving Document Layout Analysis using Patch-Text Alignment	Dec 17, 2024	Document AIDocument Image Classification	—Unverified
DONUT-hole: DONUT Sparsification by Harnessing Knowledge and Optimizing Learning Efficiency	Nov 9, 2023	document understandingKey Information Extraction	—Unverified
Books of Hours. the First Liturgical Data Set for Text Segmentation.	May 1, 2020	Handwritten Text RecognitionHTR	—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