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

Showing 576600 of 1209 papers

TitleStatusHype
Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation0
iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and RecognitionCode0
Towards Optimizing OCR for Accessibility0
Broken News: Making Newspapers Accessible to Print-Impaired0
RDU: A Region-based Approach to Form-style Document Understanding0
An Evaluation of OCR on Egocentric DataCode0
Transformer based Urdu Handwritten Text Optical Character Reader0
PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System0
Contrastive Graph Multimodal Model for Text Classification in Videos0
Two Decades of Bengali Handwritten Digit Recognition: A Survey0
Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks0
Generating Monolingual Dataset for Low Resource Language Bodo from old books using Google Keep0
A Language Modelling Approach to Quality Assessment of OCR’ed Historical Text0
An Open Source Contractual Language Understanding Application Using Machine LearningCode0
Multilingual Named Entity Recognition for Medieval Charters Using Stacked Embeddings and Bert-based Models.0
Handwritten Character Generation using Y-Autoencoder for Character Recognition Model Training0
CAMIO: A Corpus for OCR in Multiple Languages0
Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)0
Simulation d’erreurs d’OCR dans les systèmes de TAL pour le traitement de données anachroniques (Simulation of OCR errors in NLP systems for processing anachronistic data)0
Toolbox : une chaîne de traitement de corpus pour les humanités numériques (Toolbox : a corpus processing pipeline for digital humanities)0
Reconnaissance d’entités nommées sur des sorties OCR bruitées : des pistes pour la désambiguïsation morphologique automatique (Resolution of entity linking issues on noisy OCR output : automatic disambiguation tracks)0
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining0
Delivering Document Conversion as a Cloud Service with High Throughput and ResponsivenessCode2
Optical character recognition quality affects perceived usefulness of historical newspaper clippings0
hmBERT: Historical Multilingual Language Models for Named Entity RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DTrOCRAccuracy (%)89.6Unverified
2DTrOCR 105MAccuracy (%)89.6Unverified
3MaskOCR-LAccuracy (%)82.6Unverified
4TransOCRAccuracy (%)72.8Unverified
5SRNAccuracy (%)65Unverified
6MORANAccuracy (%)64.3Unverified
7SEEDAccuracy (%)61.2Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4oAverage Accuracy76.22Unverified
2Gemini-1.5 ProAverage Accuracy76.13Unverified
3Claude-3 SonnetAverage Accuracy67.71Unverified
4RapidOCRAverage Accuracy56.98Unverified
5EasyOCRAverage Accuracy49.3Unverified
#ModelMetricClaimedVerifiedStatus
1STREETSequence error27.54Unverified
2SEESequence error22Unverified
3AttentionOCR_Inception-resnet-v2_LocationSequence error15.8Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-NOPOOLBLEU89.09Unverified
2I2L-STRIPSBLEU89Unverified
#ModelMetricClaimedVerifiedStatus
1TesseractCharacter Error Rate (CER)0.08Unverified
2EasyOCRCharacter Error Rate (CER)0.07Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-STRIPSBLEU88.86Unverified