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/JingfenQiao/ColPali-Reproducibility

공식 코드

Reproducibility, Replicability, and Insights into Visual Document Retrieval with Late Interaction

https://github.com/JohnLangford/vowpal_wabbit

공식 코드

On the reusability of samples in active learning

https://github.com/JonathanWenger/pycalib

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Non-Parametric Calibration for Classification

https://github.com/JonnoB/clocrc

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CLOCR-C: Context Leveraging OCR Correction with Pre-trained Language Models

https://github.com/JonnoB/scrambledtext

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Scrambled text: training Language Models to correct OCR errors using synthetic data

https://github.com/JonnoB/scrambledtext_analysis

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Scrambled text: training Language Models to correct OCR errors using synthetic data

https://github.com/JonnoB/training_lms_with_synthetic_data

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Scrambled text: training Language Models to correct OCR errors using synthetic data

https://github.com/JordanAsh/badge

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Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

https://github.com/JustinHsu1019/KAP

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KAP: MLLM-assisted OCR Text Enhancement for Hybrid Retrieval in Chinese Non-Narrative Documents

https://github.com/Kaleidophon/deep-significance

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Cartography Active Learning

https://github.com/KhaLee2307/StrDA

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Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition

https://github.com/KirillShmilovich/ActiveLearningCG

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Discovery of Self-Assembling $π$-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

https://github.com/LCSL/incremental_multiclass_RLSC

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Incremental Robot Learning of New Objects with Fixed Update Time

https://github.com/Leedeng/Line-Counter

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LineCounter: Learning Handwritten Text Line Segmentation by Counting

https://github.com/LibraryOfCongress/newspaper-navigator

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The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America

https://github.com/Living-with-machines/DeezyMatch

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A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching

https://github.com/Living-with-machines/LwM_SIGSPATIAL2020_ToponymMatching

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A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching

https://github.com/MH-Hou/EARLY

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"Give Me an Example Like This": Episodic Active Reinforcement Learning from Demonstrations

https://github.com/MJ10/DEUP

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DEUP: Direct Epistemic Uncertainty Prediction

https://github.com/MMunibas/PhysNet_DER

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Uncertainty quantification for predictions of atomistic neural networks

https://github.com/MarkPotanin/DigitalPeter

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Digital Peter: Dataset, Competition and Handwriting Recognition Methods

https://github.com/Mars-tin/GraphPart

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Partition-Based Active Learning for Graph Neural Networks

https://github.com/MarsalekGroup/paper-c-nnp

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Committee neural network potentials control generalization errors and enable active learning

https://github.com/MattAlexMiracle/SmartPatch

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SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators

https://github.com/MengShen0709/bmmal

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Towards Balanced Active Learning for Multimodal Classification

https://github.com/MhLiao/DB

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Real-time Scene Text Detection with Differentiable Binarization

https://github.com/MiriamHu/ActiveBoundary

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Active Decision Boundary Annotation with Deep Generative Models

https://github.com/MohamedAliRashad/arabic-nougat

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Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction

https://github.com/MoriartyShan/Convolution-based-Probability-Gradient-Loss

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Convolution-based Probability Gradient Loss for Semantic Segmentation

https://github.com/MrGiovanni/Active-Learning

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Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts

https://github.com/NBAiLab/notram

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Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model

https://github.com/NTU-Siqiang-Group/CAMAL

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CAMAL: Optimizing LSM-trees via Active Learning

https://github.com/NUAA-AL/ALiPy

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ALiPy: Active Learning in Python

https://github.com/Naagar/Seeds_Classification

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Automated Seed Quality Testing System using GAN & Active Learning

https://github.com/NarayanSchuetz/SpectralLayersPyTorch

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Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

https://github.com/NeuralSentinel/DistALANER

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DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem

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