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 376–400 of 1209 papers

TitleStatusHype
Convolution-based Probability Gradient Loss for Semantic SegmentationCode0
Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based ReasoningCode0
Order-preserving Consistency Regularization for Domain Adaptation and GeneralizationCode0
Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural NetworkCode0
HENet: Forcing a Network to Think More for Font RecognitionCode0
Improving OCR Accuracy on Early Printed Books using Deep Convolutional NetworksCode0
DriveThru: a Document Extraction Platform and Benchmark Datasets for Indonesian Local Language ArchivesCode0
LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing ImagesCode0
A Tool for Facilitating OCR Postediting in Historical DocumentsCode0
A template-independent approach for information extraction in real estate documentsCode0
Analyzing Green View Index and Green View Index best path using Google Street View and deep learningCode0
GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document UnderstandingCode0
From Videos to URLs: A Multi-Browser Guide To Extract User's Behavior with Optical Character RecognitionCode0
E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene TextCode0
Brno Mobile OCR DatasetCode0
From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-CorrectionCode0
Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical recordsCode0
Gated Recurrent Convolution Neural Network for OCRCode0
A Multi-Object Rectified Attention Network for Scene Text RecognitionCode0
Handwriting Classification for the Analysis of Art-Historical DocumentsCode0
EATEN: Entity-aware Attention for Single Shot Visual Text ExtractionCode0
Quantifying Character Similarity with Vision TransformersCode0
FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text SpottingCode0
FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo EmbeddingsCode0
A Survey of Deep Learning Approaches for OCR and Document UnderstandingCode0
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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