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 1001–1050 of 1209 papers

TitleStatusHype
A Multi-faceted OCR Framework for Artificial Urdu News Ticker Text Recognition—0
French Word Recognition through a Quick Survey on Recurrent Neural Networks Using Long-Short Term Memory RNN-LSTM—0
The Monge-Kantorovich Optimal Transport Distance for Image Comparison—0
Neural Monkey: The Current State and Beyond—0
Chinese Text in the WildCode0
Improving OCR Accuracy on Early Printed Books using Deep Convolutional NetworksCode0
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active LearningCode0
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
Gated Recurrent Convolution Neural Network for OCRCode0
SuperOCR for ALTA 2017 Shared Task—0
OCR Post-Processing Text Correction using Simulated Annealing (OPTeCA)—0
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
Multi-modular domain-tailored OCR post-correction—0
Improving Document Clustering by Removing Unnatural Language—0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers—0
The Labeled Segmentation of Printed Books—0
Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding—0
Sequence-to-Label Script Identification for Multilingual OCR—0
Convolutional Neural Networks for Font Classification—0
STN-OCR: A single Neural Network for Text Detection and Text RecognitionCode0
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks—0
Text Recognition in Scene Image and Video Frame using Color Channel Selection—0
A second-order orientation-contrast stimulus for population-receptive-field-based retinotopic mapping—0
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter—0
Single Classifier-based Passive System for Source Printer Classification using Local Texture FeaturesCode0
SEARNN: Training RNNs with Global-Local LossesCode0
Text Extraction From Texture Images Using Masked Signal Decomposition—0
Traitement des Mots Hors Vocabulaire pour la Traduction Automatique de Document OCRis\'es en Arabe (This article presents a new system that automatically translates images of arabic documents)—0
Handwritten Urdu Character Recognition using 1-Dimensional BLSTM Classifier—0
Derivate-based Component-Trees for Multi-Channel Image Segmentation—0
The Making of the Royal Society Corpus—0
Tagging Named Entities in 19th Century and Modern Finnish Newspaper Material with a Finnish Semantic Tagger—0
OCR and post-correction of historical Finnish texts—0
Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur \& Antiqua Models and Image Preprocessing—0
Applying BLAST to Text Reuse Detection in Finnish Newspapers and Journals, 1771-1910—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
1DTrOCR 105MAccuracy (%)89.6—Unverified
2DTrOCRAccuracy (%)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