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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 626–650 of 1209 papers

TitleStatusHype
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps—0
Simple Transparent Adversarial Examples—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
Sinica-IASL Chinese spelling check system at Sighan-7—0
SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis—0
Slide2Text: Leveraging LLMs for Personalized Textbook Generation from PowerPoint Presentations—0
Solution for SMART-101 Challenge of ICCV Multi-modal Algorithmic Reasoning Task 2023—0
Solving Substitution Ciphers with Combined Language Models—0
Southern Newswire Corpus: A Large-Scale Dataset of Mid-Century Wire Articles Beyond the Front Page—0
SPARLING: Learning Latent Representations with Extremely Sparse Activations—0
Sparse Concept Coded Tetrolet Transform for Unconstrained Odia Character Recognition—0
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check—0
Squibs: Spelling Error Patterns in Brazilian Portuguese—0
Star-net: A spatial attention residue network for scene text recognition.—0
Statistical Learning for OCR Text Correction—0
Machine Learning Construction: implications to cybersecurity—0
Statistical Machine Translation Improvement based on Phrase Selection—0
Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks—0
STRIDE : Scene Text Recognition In-Device—0
Structured Analysis and Comparison of Alphabets in Historical Handwritten Ciphers—0
Sum-Product Networks for Sequence Labeling—0
SuperOCR: A Conversion from Optical Character Recognition to Image Captioning—0
SuperOCR for ALTA 2017 Shared Task—0
Survey of Computational Approaches to Lexical Semantic Change—0
SVDocNet: Spatially Variant U-Net for Blind Document Deblurring—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