Papers with Code 아카이브

Optical Character Recognition (OCR)

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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/kunalghosh/multi_fidelity_prediction_gp

공식 코드

Active Learning of Molecular Data for Task-Specific Objectives

https://github.com/kushalkafle/PREFIL

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Answering Questions about Data Visualizations using Efficient Bimodal Fusion

https://github.com/lackel/loop

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Generalized Category Discovery with Large Language Models in the Loop

https://github.com/lai1997/bmlp_active_public

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Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models

https://github.com/lamps-lab/AutoMeta

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Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations

https://github.com/large-ocr-model/large-ocr-model.github.io

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An Empirical Study of Scaling Law for OCR

https://github.com/laytextllm/laytextllm

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A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding

https://github.com/lbh1024/can

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When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition

https://github.com/lchen001/hapi

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HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions

https://github.com/leaplabthu/activenerf

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ActiveNeRF: Learning where to See with Uncertainty Estimation

https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks

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Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

https://github.com/learningmatter-mit/surface-sampling

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Machine-learning-accelerated simulations to enable automatic surface reconstruction

https://github.com/learningmatter-mit/uq_singlenn

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Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

https://github.com/lee-cbg/activetcr

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Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity Prediction

https://github.com/legaldocumentprocessing/fir_dataset_icdar2023

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TransDocAnalyser: A Framework for Offline Semi-structured Handwritten Document Analysis in the Legal Domain

https://github.com/leibinghe/GAAL-based-outlier-detection

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Generative Adversarial Active Learning for Unsupervised Outlier Detection

https://github.com/leitro/selfattnscoring-mpdocvqa

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Multi-Page Document Visual Question Answering using Self-Attention Scoring Mechanism

https://github.com/lenarttreven/dgm

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Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models

https://github.com/lfilstro/hitl-abc

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Approximate Bayesian Computation with Domain Expert in the Loop

https://github.com/lgordon99/rhino-midden-detector

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Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats

https://github.com/liangjiubujiu/ctooth

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CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation

https://github.com/licongguan/ilm-assl

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Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentation

https://github.com/lighterswang/awesome-active-learning-for-medical-image-analysis

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A comprehensive survey on deep active learning in medical image analysis

https://github.com/linkon87/exploring-diversity-based-active-learning-for-3d-object-detection-in-autonomous-driving

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Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving

https://github.com/lioujheyu/cuda_evolve

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GEVO: GPU Code Optimization using Evolutionary Computation

https://github.com/liu-jc/lscale

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LSCALE: Latent Space Clustering-Based Active Learning for Node Classification

https://github.com/liushangnoname/uncertainty-sampling

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Understanding Uncertainty Sampling

https://github.com/llabres/library-dataset

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Image-text matching for large-scale book collections

https://github.com/llnl/gplasdi

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Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations

https://github.com/lluisgomez/script_identification

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Improving patch-based scene text script identification with ensembles of conjoined networks

https://github.com/lmu-dbs/diffusal

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DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification

https://github.com/lolipopshock/Detectron2_AL

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OLALA: Object-Level Active Learning for Efficient Document Layout Annotation

https://github.com/lorenzo-perini/ballad

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How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection

https://github.com/louzounlab/graph-al

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Regional based query in graph active learning

https://github.com/lucasalegre/morl-baselines

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Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

https://github.com/luckeciano/bal-pm

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Deep Bayesian Active Learning for Preference Modeling in Large Language Models

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