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
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting—0
DEXTER: An end-to-end system to extract table contents from electronic medical health documents—0
GMN: Generative Multi-modal Network for Practical Document Information Extraction—0
Towards Multimodal Vision-Language Models Generating Non-Generic Text—0
BusiNet -- a Light and Fast Text Detection Network for Business Documents—0
Sequence-aware multimodal page classification of Brazilian legal documentsCode0
Challenging America: Modeling language in longer time scales—0
Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation—0
iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and RecognitionCode0
Broken News: Making Newspapers Accessible to Print-Impaired—0
Towards Optimizing OCR for Accessibility—0
RDU: A Region-based Approach to Form-style Document Understanding—0
An Evaluation of OCR on Egocentric DataCode0
Transformer based Urdu Handwritten Text Optical Character Reader—0
PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System—0
Contrastive Graph Multimodal Model for Text Classification in Videos—0
Two Decades of Bengali Handwritten Digit Recognition: A Survey—0
Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks—0
A Language Modelling Approach to Quality Assessment of OCR’ed Historical Text—0
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining—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
An Open Source Contractual Language Understanding Application Using Machine LearningCode0
Handwritten Character Generation using Y-Autoencoder for Character Recognition Model Training—0
CAMIO: A Corpus for OCR in Multiple Languages—0
Multilingual Named Entity Recognition for Medieval Charters Using Stacked Embeddings and Bert-based Models.—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