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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/tjunlp-lab/aprt

공식 코드

Automated Progressive Red Teaming

https://github.com/tkanchin/adaptive_reg_active_learning

공식 코드

A Simple yet Brisk and Efficient Active Learning Platform for Text Classification

https://github.com/tobiasvanderwerff/master-thesis

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Writer adaptation for offline text recognition: An exploration of neural network-based methods

https://github.com/tobyperrett/epic-text-annotations

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An Evaluation of OCR on Egocentric Data

https://github.com/tom-andersson/eds2022-convgnp-sensor-placement

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Environmental Sensor Placement with Convolutional Gaussian Neural Processes

https://github.com/tongshuangwu/scattershot

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ScatterShot: Interactive In-context Example Curation for Text Transformation

https://github.com/topdu/openocr

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SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition

https://github.com/topoxlab/consistency-ranking-loss

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Confidence Estimation Using Unlabeled Data

https://github.com/torbensdjohansen/hana

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HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition

https://github.com/tpopordanoska/calibration_and_bias

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On the relationship between calibrated predictors and unbiased volume estimation

https://github.com/trent-b/unisel

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Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection

https://github.com/trung6/ActLogReg

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Near-Polynomially Competitive Active Logistic Regression

https://github.com/tsa87/TacoGFN-SBDD

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TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design

https://github.com/tsunghan-wu/d2ada

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D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

https://github.com/tsunghan-wu/redal

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ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation

https://github.com/tueboesen/a-optimal-active-learning

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A-Optimal Active Learning

https://github.com/tyxsspa/anytext

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AnyText: Multilingual Visual Text Generation And Editing

https://github.com/tzshi/dpp-al-parsing-naacl21

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Diversity-Aware Batch Active Learning for Dependency Parsing

https://github.com/ucalgary-ilab/augmented-math

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Augmented Math: Authoring AR-Based Explorable Explanations by Augmenting Static Math Textbooks

https://github.com/ucdvision/low-budget-al

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A Simple Baseline for Low-Budget Active Learning

https://github.com/ucsc-real/fairnesswithoutharm

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Fairness Without Harm: An Influence-Guided Active Sampling Approach

https://github.com/udellgroup/oboe

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OBOE: Collaborative Filtering for AutoML Model Selection

https://github.com/uds-lsv/human

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HUMAN: Hierarchical Universal Modular ANnotator

https://github.com/uhlerlab/actlearn_optint

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Active Learning for Optimal Intervention Design in Causal Models

https://github.com/uhlerlab/causal_mean_matching

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Matching a Desired Causal State via Shift Interventions

https://github.com/ultratykis/guiding-dnns-attention

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Efficient Human-in-the-loop System for Guiding DNNs Attention

https://github.com/unagpal/Active-Learning-in-CNNs-via-Expected-Improvement-Maximization

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Active Learning in CNNs via Expected Improvement Maximization

https://github.com/update-for-integrated-business-ai/coru

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CORU: Comprehensive Post-OCR Parsing and Receipt Understanding Dataset

https://github.com/uq-neusoft-health-data-science/activeea

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ActiveEA: Active Learning for Neural Entity Alignment

https://github.com/uqhwen2/FCCM

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Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective

https://github.com/uqhwen2/MACAL

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Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation

https://github.com/usccolumbia/materialsuq

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Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

https://github.com/v-laurent/vrd-image-text-generator

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Spanish TrOCR: Leveraging Transfer Learning for Language Adaptation

https://github.com/vafaei-ar/drama

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A Flexible Framework for Anomaly Detection via Dimensionality Reduction

https://github.com/vaishwarya96/GroVE

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Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models

https://github.com/valentinmargraf/activelearningpipelines

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ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

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