SOTAVerified

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 426–450 of 1209 papers

TitleStatusHype
Optimizing the Neural Network Training for OCR Error Correction of Historical Hebrew Texts—0
Toward a Period-Specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts—0
Augmented Math: Authoring AR-Based Explorable Explanations by Augmenting Static Math TextbooksCode0
Multi-Granularity Prediction with Learnable Fusion for Scene Text Recognition—0
MataDoc: Margin and Text Aware Document Dewarping for Arbitrary Boundary—0
A comparative analysis of SRGAN models—0
Modular Multimodal Machine Learning for Extraction of Theorems and Proofs in Long Scientific Documents (Extended Version)Code1
Handwritten and Printed Text Segmentation: A Signature Case Study—0
Handwritten Text Recognition Using Convolutional Neural Network—0
A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing—0
Artificial Eye for the Blind—0
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding—0
Estimating Post-OCR Denoising Complexity on Numerical Texts—0
Fraunhofer SIT at CheckThat! 2023: Mixing Single-Modal Classifiers to Estimate the Check-Worthiness of Multi-Modal Tweets—0
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
UTRNet: High-Resolution Urdu Text Recognition In Printed DocumentsCode1
Resume Information Extraction via Post-OCR Text Processing—0
A Survey on Multimodal Large Language Models—0
Document Image Cleaning using Budget-Aware Black-Box ApproximationCode0
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
Weakly supervised information extraction from inscrutable handwritten document images—0
When Vision Fails: Text Attacks Against ViT and OCRCode0
SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure CaptioningCode0
Transformer-Based UNet with Multi-Headed Cross-Attention Skip Connections to Eliminate Artifacts in Scanned Documents—0
TransDocAnalyser: A Framework for Offline Semi-structured Handwritten Document Analysis in the Legal DomainCode1
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All datasetsBenchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical StudyVideoDB's OCR Benchmark Public CollectionFSNS - TestI2L-140KSUTim2latex-100k

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DTrOCRAccuracy (%)89.6—Unverified
2DTrOCR 105MAccuracy (%)89.6—Unverified
3MaskOCR-LAccuracy (%)82.6—Unverified
4TransOCRAccuracy (%)72.8—Unverified
5SRNAccuracy (%)65—Unverified
6MORANAccuracy (%)64.3—Unverified
7SEEDAccuracy (%)61.2—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4oAverage Accuracy76.22—Unverified
2Gemini-1.5 ProAverage Accuracy76.13—Unverified
3Claude-3 SonnetAverage Accuracy67.71—Unverified
4RapidOCRAverage Accuracy56.98—Unverified
5EasyOCRAverage Accuracy49.3—Unverified
#ModelMetricClaimedVerifiedStatus
1STREETSequence error27.54—Unverified
2SEESequence error22—Unverified
3AttentionOCR_Inception-resnet-v2_LocationSequence error15.8—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-NOPOOLBLEU89.09—Unverified
2I2L-STRIPSBLEU89—Unverified
#ModelMetricClaimedVerifiedStatus
1TesseractCharacter Error Rate (CER)0.08—Unverified
2EasyOCRCharacter Error Rate (CER)0.07—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-STRIPSBLEU88.86—Unverified