Papers with Code 아카이브

Optical Character Recognition (OCR)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 10
평가표: 6
보존된 평가 기록: 20

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)

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/applicaai/lambert

공식 코드

LAMBERT: Layout-Aware (Language) Modeling for information extraction

https://github.com/applied-ai-research-lab/orchestrator-agent-trust

공식 코드

Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning

https://github.com/apurba-nsu-rnd-lab/lila-boti

공식 코드

LILA-BOTI : Leveraging Isolated Letter Accumulations By Ordering Teacher Insights for Bangla Handwriting Recognition

https://github.com/ariapoy/active-learning-benchmark

공식 코드

Re-Benchmarking Pool-Based Active Learning for Binary Classification

https://github.com/arpanbiswas52/imfbo_ising

공식 코드

Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities

https://github.com/arpanbiswas52/vartbo

공식 코드

A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments

https://github.com/arthur-thuy/qde-active-learning

공식 코드

Active Learning to Guide Labeling Efforts for Question Difficulty Estimation

https://github.com/arvindmvepa/al-seg

공식 코드

Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation

https://github.com/asaran/vessal

공식 코드

Streaming Active Learning with Deep Neural Networks

https://github.com/ask-berkeley/stable-training

공식 코드

Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

https://github.com/asnijders/multi_source_al

공식 코드

Investigating Multi-source Active Learning for Natural Language Inference

https://github.com/aspuru-guzik-group/janus

공식 코드

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design

https://github.com/asreview/paper-asreview-introduction

공식 코드

Open Source Software for Efficient and Transparent Reviews

https://github.com/astrolabsoftware/fink-science

공식 코드

Finding active galactic nuclei through Fink

https://github.com/atomistic-ml/ani-al

공식 코드

Automated discovery of a robust interatomic potential for aluminum

https://github.com/avakanski/creep-uncertainty-quantification

공식 코드

Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks

https://github.com/avihu111/typiclust

공식 코드

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

https://github.com/ayushbits/pe-ocr-sanskrit

공식 코드

A Benchmark and Dataset for Post-OCR text correction in Sanskrit

https://github.com/baal-org/baal

공식 코드

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning

https://github.com/bardisafa/eoal

공식 코드

Entropic Open-set Active Learning

https://github.com/bargenossar/the-battleship-approach-to-al-of-em-problem

공식 코드

The Battleship Approach to the Low Resource Entity Matching Problem

https://github.com/barleah/greedyal

공식 코드

Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval

https://github.com/batistagroup/chemspaceal

공식 코드

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

https://github.com/bdevl/SMO

공식 코드

Self-supervised optimization of random material microstructures in the small-data regime

https://github.com/beacandler/EATEN

공식 코드

EATEN: Entity-aware Attention for Single Shot Visual Text Extraction

https://github.com/beatrizmoya/rlfluidperception

공식 코드

A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids

https://github.com/belakaria/al-gsa-dgsms

공식 코드

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes

https://github.com/belindal/discrete-active-learning-coref

공식 코드

Active Learning for Coreference Resolution using Discrete Annotation

https://github.com/bemporad/gnep-learn

공식 코드

An active learning method for solving competitive multi-agent decision-making and control problems

https://github.com/benavoli/SkewGP

공식 코드

A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes

https://github.com/bflameswift/uni-mumer

공식 코드

Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition

https://github.com/bhavyaghai/fluent

공식 코드

Fluent: An AI Augmented Writing Tool for People who Stutter

https://github.com/bidur-khanal/mvaal-medical-images

공식 코드

M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks

https://github.com/bit-da/duc

공식 코드

Dirichlet-based Uncertainty Calibration for Active Domain Adaptation

https://github.com/bit-da/eada

공식 코드

Active Learning for Domain Adaptation: An Energy-Based Approach

https://github.com/bit-da/ripu

공식 코드

Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.