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/Sid2697/Word-recognition-and-retrieval

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Fused Text Recogniser and Deep Embeddings Improve Word Recognition and Retrieval

https://github.com/SineZHAN/deepALplus

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A Comparative Survey of Deep Active Learning

https://github.com/SkafteNicki/john

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Reliable training and estimation of variance networks

https://github.com/Souvic/aspect-based-sentiment-analysis-of-scientific-reviews-jcdl

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Aspect-based Sentiment Analysis of Scientific Reviews

https://github.com/Stanford-ILIAD/DPP-Batch-Active-Learning

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Batch Active Learning Using Determinantal Point Processes

https://github.com/Stanford-ILIAD/batch-active-preference-based-learning

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Batch Active Preference-Based Learning of Reward Functions

https://github.com/SteffenCzolbe/probabilistic_segmentation

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Is segmentation uncertainty useful?

https://github.com/Sufianlab/BDNet

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BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks

https://github.com/SvenL13/LocalValidity

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Quantifying Local Model Validity using Active Learning

https://github.com/Sy-Zhang/MMC-PCFG

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Video-aided Unsupervised Grammar Induction

https://github.com/System-T/PARTNER

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Learning Structured Representations of Entity Names using Active Learning and Weak Supervision

https://github.com/ThuongTNguyen/ALchemist

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On the Fragility of Active Learners for Text Classification

https://github.com/TonyLianLong/UnsupervisedSelectiveLabeling

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Unsupervised Selective Labeling for More Effective Semi-Supervised Learning

https://github.com/UKPLab/acl2020-empowering-active-learning

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Empowering Active Learning to Jointly Optimize System and User Demands

https://github.com/UKPLab/arxiv2018-bayesian-ensembles

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A Bayesian Approach for Sequence Tagging with Crowds

https://github.com/UKPLab/emnlp2018-april

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APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

https://github.com/UKPLab/irj-neural-april

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Preference-based Interactive Multi-Document Summarisation

https://github.com/UKPLab/tacl2018-preference-convincing

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Finding Convincing Arguments Using Scalable Bayesian Preference Learning

https://github.com/Ucas-HaoranWei/Vary

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Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models

https://github.com/VRM1/ActiveLearning

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Efficacy of Bayesian Neural Networks in Active Learning

https://github.com/Vanint/OA3

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Online Adaptive Asymmetric Active Learning with Limited Budgets

https://github.com/WILAB-HIT/Resources

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Building a comprehensive syntactic and semantic corpus of Chinese clinical texts

https://github.com/Wang-Tianwei/Decoupled-attention-network

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Decoupled Attention Network for Text Recognition

https://github.com/Wang-Tianwei/Implicit-feature-alignment

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Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter

https://github.com/WenboRen/Topk-Ranking-from-Pairwise-Comparisons

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The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons

https://github.com/XavierOwen/ALC-AEGP

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Active Learning for Manifold Gaussian Process Regression

https://github.com/Xilinx/LSTM-PYNQ

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FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs

https://github.com/Xtra-Computing/thundersvm

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Sampling Bias in Deep Active Classification: An Empirical Study

https://github.com/YazhouTUD/MVAL

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A Variance Maximization Criterion for Active Learning

https://github.com/YazhouTUD/single_shot_AL

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Single Shot Active Learning using Pseudo Annotators

https://github.com/YeolJ00/faster-rcnn-pytorch

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Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images

https://github.com/YesianRohn/TextSSR

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TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition

https://github.com/YilunZhou/optimal-active-learning

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Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms

https://github.com/YoungTimmy/SAG

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Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

https://github.com/ZephyrZhuQi/ssbaseline

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Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps

https://github.com/ZiTingW/asvp

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Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models

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