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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 601–650 of 1209 papers

TitleStatusHype
SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing—0
SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition—0
Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents—0
Scaling Automatic Extraction of Pseudocode—0
Scatteract: Automated extraction of data from scatter plots—0
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering—0
Scene Text recognition with Full Normalization—0
SceneVTG++: Controllable Multilingual Visual Text Generation in the Wild—0
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings—0
Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models—0
Seeing the Signs: A Survey of Edge-Deployable OCR Models for Billboard Visibility Analysis—0
See then Tell: Enhancing Key Information Extraction with Vision Grounding—0
SEE: Towards Semi-SupervisedEnd-to-End Scene Text Recognition—0
Segmentation-free Connectionist Temporal Classification loss based OCR Model for Text Captcha Classification—0
Self-paced learning to improve text row detection in historical documents with missing labels—0
Self-supervised Data Bootstrapping for Deep Optical Character Recognition of Identity Documents—0
Semantic rule Web-based Diagnosis and Treatment of Vector-Borne Diseases using SWRL rules—0
Semantic Segmentation for Real-World and Synthetic Vehicle's Forward-Facing Camera Images—0
Semi-automated annotation of page-based documents within the Genre and Multimodality framework—0
Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching—0
Sequence-to-Label Script Identification for Multilingual OCR—0
Sequence to Sequence Learning for Optical Character Recognition—0
Sequence-to-Sequence Pre-training with Unified Modality Masking for Visual Document Understanding—0
Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI—0
Similar Document Template Matching Algorithm—0
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
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