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/mkandai/llms-in-the-loop

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LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages

https://github.com/ml-postech/adaptive-superpixel-for-active-learning-in-semantic-segmentation

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Adaptive Superpixel for Active Learning in Semantic Segmentation

https://github.com/mlpc-ucsd/bliva

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BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions

https://github.com/mlpeschl/moral_rl

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MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning

https://github.com/mlsysops/active-learning-as-a-service

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Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI

https://github.com/mlsysops/alaas

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Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI

https://github.com/mmilkova/nlp-values

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Detecting value-expressive text posts in Russian social media

https://github.com/mmjing/ocr

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Order-preserving Consistency Regularization for Domain Adaptation and Generalization

https://github.com/mnamysl/nat-acl2020

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NAT: Noise-Aware Training for Robust Neural Sequence Labeling

https://github.com/mnamysl/nat-acl2021

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Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling

https://github.com/modAL-python/modAL

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modAL: A modular active learning framework for Python

https://github.com/modelscope/ms-swift

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SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning

https://github.com/modelscope/swift

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SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning

https://github.com/mohaimenz/deep_active_featl

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Deep Active Audio Feature Learning in Resource-Constrained Environments

https://github.com/mojtaba-Hsafa/OCR-browser-domain-extractor

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From Videos to URLs: A Multi-Browser Guide To Extract User's Behavior with Optical Character Recognition

https://github.com/moonshotai/kimi-vl

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Kimi-VL Technical Report

https://github.com/mourga/contrastive-active-learning

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On the Importance of Effectively Adapting Pretrained Language Models for Active Learning

https://github.com/mrgiovanni/abdomenatlas

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AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks

https://github.com/mrottmann/DeepBASS

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Deep Bayesian Active Semi-Supervised Learning

https://github.com/msfasha/TextImagesToolkit

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A Hybrid Deep Learning Model for Arabic Text Recognition

https://github.com/mtkresearch/generative-fusion-decoding

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Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Multi-modal Text Recognition

https://github.com/multimodal-art-projection/I-SHEEP

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I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm

https://github.com/multimodal-art-projection/criticlean

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CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization

https://github.com/munanning/mada

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Multi-Anchor Active Domain Adaptation for Semantic Segmentation

https://github.com/mv96/mm_extraction

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Modular Multimodal Machine Learning for Extraction of Theorems and Proofs in Long Scientific Documents (Extended Version)

https://github.com/mwalmsley/galaxy-zoo-bayesian-cnn

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Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

https://github.com/mwerner28/dmgt

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Optimal Data Selection: An Online Distributed View

https://github.com/mwien/counting-with-applications

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Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

https://github.com/mxschmdt/mrqa-gen-al

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Combining Data Generation and Active Learning for Low-Resource Question Answering

https://github.com/nab170130/new_streamline

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STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings

https://github.com/nab170130/stencil

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STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning

https://github.com/nadavborenstein/pixel-bw

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PHD: Pixel-Based Language Modeling of Historical Documents

https://github.com/namkoong-lab/inductive-biases-exchangeable-sequence

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Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling

https://github.com/natliblux/nautilusocr

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Combining Morphological and Histogram based Text Line Segmentation in the OCR Context

https://github.com/naver/popeval

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PopEval: A Character-Level Approach to End-To-End Evaluation Compatible with Word-Level Benchmark Dataset

https://github.com/ncoop57/tango

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It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports

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