Papers with Code 아카이브
Optical Character Recognition (OCR)
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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)
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
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SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition
Predicting the Past: Estimating Historical Appraisals with OCR and Machine Learning
Synthetic Document Question Answering in Hungarian
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition
ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge
VidText: Towards Comprehensive Evaluation for Video Text Understanding
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?
E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning
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