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 651–675 of 1209 papers

TitleStatusHype
Ice hockey player identification via transformers and weakly supervised learning—0
Discriminative Dictionary Learning based on Statistical Methods—0
Indian Licence Plate Dataset in the wildCode1
Handwritten Digit Recognition Using Improved Bounding Box Recognition Technique—0
Lexically Aware Semi-Supervised Learning for OCR Post-CorrectionCode1
BART for Post-Correction of OCR Newspaper Text—0
Unsupervised Multi-View Post-OCR Error Correction With Language Models—0
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check—0
Named Entity Recognition in Historic Legal Text: A Transformer and State Machine Ensemble Method—0
DocScanner: Robust Document Image Rectification with Progressive LearningCode1
DocTr: Document Image Transformer for Geometric Unwarping and Illumination CorrectionCode1
Ultra Light OCR Competition Technical Report—0
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned TextsCode0
HENet: Forcing a Network to Think More for Font RecognitionCode0
Learning UI Navigation through Demonstrations composed of Macro Actions—0
Optical Character Recognition of 19th Century Classical Commentaries: the Current State of AffairsCode0
Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks—0
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech RecognitionCode1
Rerunning OCR: A Machine Learning Approach to Quality Assessment and Enhancement PredictionCode1
Asking questions on handwritten document collections—0
A Proposal of Automatic Error Correction in Text—0
TrOCR: Transformer-based Optical Character Recognition with Pre-trained ModelsCode1
Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction—0
Adapting the Tesseract Open-Source OCR Engine for Tamil and Sinhala Legacy Fonts and Creating a Parallel Corpus for Tamil-Sinhala-EnglishCode0
Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence ModelsCode1
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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