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/NilBiescas/GeoContrastNet

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GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document Understanding

https://github.com/NoelShin/PixelPick

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All you need are a few pixels: semantic segmentation with PixelPick

https://github.com/OPPO-Mente-Lab/GlyphDraw

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GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation

https://github.com/OmarSababha/active_ML

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Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation

https://github.com/OpenBMB/MiniCPM-o

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MiniCPM-V: A GPT-4V Level MLLM on Your Phone

https://github.com/PV-Lab/Benchmarking

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Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

https://github.com/PaddlePaddle/PaddleMIX

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PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks

https://github.com/PaddlePaddle/PaddleOCR

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PP-OCR: A Practical Ultra Lightweight OCR System

https://github.com/PaddlePaddle/Quantum

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VSQL: Variational Shadow Quantum Learning for Classification

https://github.com/PaddlePaddle/VIMER/tree/main/StrucTexT/v2

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StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training

https://github.com/Pay20Y/SEED

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SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition

https://github.com/Pearl-UTexas/ActiveVaR

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Risk-Aware Active Inverse Reinforcement Learning

https://github.com/PengyiZhang/MIADeepSSL

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A Survey on Deep Learning of Small Sample in Biomedical Image Analysis

https://github.com/PhilipCorr/numeral-gesture-dataset

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Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens

https://github.com/Pleias/toxic-commons

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Toxicity of the Commons: Curating Open-Source Pre-Training Data

https://github.com/Psarpei/Multi-Type-TD-TSR

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Multi-Type-TD-TSR -- Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations

https://github.com/Quicksign/ocrized-text-dataset

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Multimodal deep networks for text and image-based document classification

https://github.com/QuwsarOhi/BanglaWriting

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BanglaWriting: A multi-purpose offline Bangla handwriting dataset

https://github.com/QuwsarOhi/bengali_word_ocr

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End-to-End Optical Character Recognition for Bengali Handwritten Words

https://github.com/RQLuo/MixTeX

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MixTex: Unambiguous Recognition Should Not Rely Solely on Real Data

https://github.com/RebinMA/KHCR

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

https://github.com/RelationRx/pyrelational

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PyRelationAL: a python library for active learning research and development

https://github.com/RemiLeblond/SeaRNN-open

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SEARNN: Training RNNs with Global-Local Losses

https://github.com/RishiHazra/Actively-reducing-redundancies-in-Active-Learning-for-Sequence-Tagging

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Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation

https://github.com/RookieRQ/AL-enhanced-Surrogate_MLMC-Adequacy_Assessment

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MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models

https://github.com/RotsteinNoam/FuseCap

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FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions

https://github.com/RussellXie7/motion-based-handwriting-recognition

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Motion-Based Handwriting Recognition

https://github.com/SALT-NLP/LLaVAR

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LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

https://github.com/SPEECHCOG/cpc_pytorch

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Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition

https://github.com/SUNCAT-Center/CatLearn

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An Atomistic Machine Learning Package for Surface Science and Catalysis

https://github.com/Sagarika-Singh-99/indonesian_manhwa_translation

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Crossing Language Borders: A Pipeline for Indonesian Manhwa Translation

https://github.com/SamanthaBiegel/ActiveWeaSuL

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Active WeaSuL: Improving Weak Supervision with Active Learning

https://github.com/SciEcon/Active_Learning

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The Right Tool for the Job: Matching Active Learning Techniques to Learning Objectives

https://github.com/Seondong/Customs-Fraud-Detection

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Active Learning for Human-in-the-Loop Customs Inspection

https://github.com/Shen-Lab/BAL

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Bayesian active learning for optimization and uncertainty quantification in protein docking

https://github.com/ShengdingHu/GraphPolicyNetworkActiveLearning

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Graph Policy Network for Transferable Active Learning on Graphs

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