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/nec-research/alebrew

공식 코드

Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials

https://github.com/neptunebio/naiad

공식 코드

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space

https://github.com/nercms-mmap/caaf

공식 코드

Confidence-Aware Active Feedback for Interactive Instance Search

https://github.com/nerminsamet/seedal

공식 코드

You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation

https://github.com/netflix/videoannotator

공식 코드

Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learning

https://github.com/neuhai/explanation-enriched-active-learning

공식 코드

Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture

https://github.com/neulab/cmulab

공식 코드

CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models

https://github.com/neuralchecker/pyModelExtractor

공식 코드

Towards Efficient Active Learning of PDFA

https://github.com/neuralchecker/pymodelextractor_towards_efficient_active_learning_of_PDFA_experiments

공식 코드

Towards Efficient Active Learning of PDFA

https://github.com/neuropoly/deep-active-learning

공식 코드

Deep Active Learning for Axon-Myelin Segmentation on Histology Data

https://github.com/newslotdetection/newslotdetection

공식 코드

Actively Discovering New Slots for Task-oriented Conversation

https://github.com/newspanda-wwf-cmu/weekly-pipeline

공식 코드

NewsPanda: Media Monitoring for Timely Conservation Action

https://github.com/niansong1996/wassp

공식 코드

Merging Weak and Active Supervision for Semantic Parsing

https://github.com/nickboucher/diacritics

공식 코드

When Vision Fails: Text Attacks Against ViT and OCR

https://github.com/niclasgriesshaber/gemini_historical_dataset_pipeline

공식 코드

Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents

https://github.com/niclasgriesshaber/llm_historical_dataset_benchmarking

공식 코드

Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents

https://github.com/nihalsid/ViewAL

공식 코드

ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation

https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts

공식 코드

Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure

https://github.com/nikita-0209/mf-al-gfn

공식 코드

Multi-Fidelity Active Learning with GFlowNets

https://github.com/nikoguan/p_ocr

공식 코드

Advancing Post-OCR Correction: A Comparative Study of Synthetic Data

https://github.com/nikolajkb/furiganadetection

공식 코드

Detection of Furigana Text in Images

https://github.com/ningkp/LfOSA

공식 코드

Active Learning for Open-set Annotation

https://github.com/nirjhar-das/active-preference-optimization

공식 코드

Active Preference Optimization for Sample Efficient RLHF

https://github.com/nju-websoft/daakg

공식 코드

Deep Active Alignment of Knowledge Graph Entities and Schemata

https://github.com/nkees/active-learning-argument-strength

공식 코드

Active Learning for Argument Strength Estimation

https://github.com/nlp-anonymous-researcher/legal-uqa

공식 코드

LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question Answering

https://github.com/nlp-uoregon/famie

공식 코드

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

https://github.com/nlpAThits/BioNLP2021

공식 코드

Word-Level Alignment of Paper Documents with their Electronic Full-Text Counterparts

https://github.com/noabdavid/activeBNSL

공식 코드

Active Structure Learning of Bayesian Networks in an Observational Setting

https://github.com/noyomer/rfan-trial

공식 코드

Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions

https://github.com/nphdang/ccral

공식 코드

Efficient Classification with Counterfactual Reasoning and Active Learning

https://github.com/ntu-aiot/ObfNet

공식 코드

Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference

https://github.com/ntucllab/libact

공식 코드

libact: Pool-based Active Learning in Python

https://github.com/nus-hpc-ai-lab/info-coevolution

공식 코드

Info-Coevolution: An Efficient Framework for Data Model Coevolution

https://github.com/nvlabs/al-mdn

공식 코드

Active Learning for Deep Object Detection via Probabilistic Modeling

https://github.com/nwaftp23/nflows_epistemic

공식 코드

Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling

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