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 1001–1025 of 1209 papers

TitleStatusHype
Chinese Text in the WildCode0
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active LearningCode0
Improving OCR Accuracy on Early Printed Books using Deep Convolutional NetworksCode0
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO DetectorCode1
Fooling OCR Systems with Adversarial Text Images—0
Teaching Machines to Code: Neural Markup Generation with Visual AttentionCode0
E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene TextCode0
Text Extraction and Retrieval from Smartphone Screenshots: Building a Repository for Life in Media—0
A Novel Approach to Skew-Detection and Correction of English Alphabets for OCR—0
Transfer Learning for OCRopus Model Training on Early Printed BooksCode0
SEE: Towards Semi-SupervisedEnd-to-End Scene Text Recognition—0
Overview of the 2017 ALTA Shared Task: Correcting OCR Errors—0
OCR Post-Processing Text Correction using Simulated Annealing (OPTeCA)—0
SuperOCR for ALTA 2017 Shared Task—0
Gated Recurrent Convolution Neural Network for OCRCode0
Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and VotingCode0
Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application—0
CryptoDL: Deep Neural Networks over Encrypted Data—0
AON: Towards Arbitrarily-Oriented Text RecognitionCode0
Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model—0
Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features—0
Linear-Time Sequence Classification using Restricted Boltzmann Machines—0
A Survey on Optical Character Recognition System—0
A Diachronic Corpus for Romanian (RoDia)—0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers—0
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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