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 701–725 of 1209 papers

TitleStatusHype
DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts—0
Self-paced learning to improve text row detection in historical documents with missing labels—0
An Assessment of the Impact of OCR Noise on Language Models—0
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition—0
Classroom Slide Narration System—0
Legal Entity Extraction using a Pointer Generator Network—0
Improve Sentence Alignment by Divide-and-conquer—0
SAFL: A Self-Attention Scene Text Recognizer with Focal LossCode0
Intelligent Document Processing -- Methods and Tools in the real world—0
Challenging America: Modeling language in longer time scales—0
Lesan -- Machine Translation for Low Resource Languages—0
Tracing Text Provenance via Context-Aware Lexical Substitution—0
Modelling Lips-State Detection Using CNN for Non-Verbal Communications—0
A Survey on Deep learning based Document Image Enhancement—0
On-Device Spatial Attention based Sequence Learning Approach for Scene Text Script Identification—0
Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?—0
Image preprocessing and modified adaptive thresholding for improving OCR—0
Ice hockey player identification via transformers and weakly supervised learning—0
Discriminative Dictionary Learning based on Statistical Methods—0
Handwritten Digit Recognition Using Improved Bounding Box Recognition Technique—0
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check—0
Unsupervised Multi-View Post-OCR Error Correction With Language Models—0
Named Entity Recognition in Historic Legal Text: A Transformer and State Machine Ensemble Method—0
BART for Post-Correction of OCR Newspaper Text—0
Ultra Light OCR Competition Technical Report—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