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 826–850 of 1209 papers

TitleStatusHype
Tamil Vowel Recognition With Augmented MNIST-like Data Set—0
Exploiter des mod\`eles de langue pour \'evaluer des sorties de logiciels d'OCR pour des documents fran du XVIIe si\`ecle ()—0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
Structured Multimodal Attentions for TextVQACode1
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text RecognitionCode1
NAT: Noise-Aware Training for Robust Neural Sequence LabelingCode1
Large Scale Font Independent Urdu Text Recognition SystemCode1
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts—0
Quantitative Analysis of Image Classification Techniques for Memory-Constrained Devices—0
Deep Learning Based Vehicle Tracking System Using License Plate Detection And Recognition—0
A Hybrid Swarm and Gravitation based feature selection algorithm for Handwritten Indic Script Classification problem—0
Development of a New Image-to-text Conversion System for Pashto, Farsi and Traditional Chinese—0
A Gaussian Process Upsampling Model for Improvements in Optical Character RecognitionCode0
The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling AmericaCode1
Books of Hours. the First Liturgical Data Set for Text Segmentation.—0
How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR—0
Time-Aware Word Embeddings for Three Lebanese News ArchivesCode0
Building OCR/NER Test Collections—0
Constructing a Public Meeting Corpus—0
Preserving Semantic Information from Old Dictionaries: Linking Senses of the `Altfranz\"osisches W\"orterbuch' to WordNet—0
OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation—0
MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters—0
A Skip-connected Multi-column Network for Isolated Handwritten Bangla Character and Digit recognitionCode0
A Tool for Facilitating OCR Postediting in Historical DocumentsCode0
Image Processing Based Scene-Text Detection and Recognition with Tesseract—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