https://github.com/stevenireeves/GP_upsamp
A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition
Papers with Code 아카이브
Optical Character Recognition (OCR)
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- 보존된 평가 기록
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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/sumitsk/MOGP-AL
Near-Optimal Active Learning of Multi-Output Gaussian Processes
https://github.com/sunnyyzj/morl-baselines
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
https://github.com/suvarna-kadam/Oct2018_Demo
https://github.com/svdesai/coreset-al
Active Learning for Convolutional Neural Networks: A Core-Set Approach
https://github.com/sw-scot/foco-mlmis
https://github.com/swanjing/Contextual-Bandits-Active-Learning
https://github.com/swapnamoy17/EAST-DenseNet
https://github.com/swarooprm/DQI
https://github.com/tanmaysheoran/CRNN-Sequence-Text-Recognition
https://github.com/tanyanair/segmentation_uncertainty
https://github.com/tej-prash/GeneExpression
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
https://github.com/telegraphic/PyGSM
A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz
https://github.com/tensorflow/models/tree/master/research/attention_ocr
Attention-based Extraction of Structured Information from Street View Imagery
https://github.com/tfzhou/protoseg
Exploring Cross-Image Pixel Contrast for Semantic Segmentation
https://github.com/thibaultvt/Diard
DiT: Self-supervised Pre-training for Document Image Transformer
https://github.com/tmatha/lstm
https://github.com/tmfs10/ical
https://github.com/tobiasvanderwerff/MetaHTR
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
https://github.com/tobiasvanderwerff/full-page-handwriting-recognition
Full Page Handwriting Recognition via Image to Sequence Extraction
https://github.com/tongjingqi/code2logic
https://github.com/tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme
https://github.com/tranbahien/CTC-OCR
https://github.com/tsingqaq/trieste
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
https://github.com/tuanio/image2latex
https://github.com/tueboesen/Active-Learning
Cost-Effective Active Learning for Deep Image Classification
https://github.com/tutujingyugang1/ChatVLA_public
ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge
https://github.com/uakarsh/docformerv2
https://github.com/uakarsh/latr
https://github.com/ucas-haoranwei/got-ocr2.0
General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
https://github.com/ugrulas/DivNoising
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
https://github.com/ungeimer/Etiquetadorv1
Active learning in annotating micro-blogs dealing with e-reputation
https://github.com/ungeimer/FLAT-TextTagger
Active learning in annotating micro-blogs dealing with e-reputation
https://github.com/ungeimer/FLAT-lite
Active learning in annotating micro-blogs dealing with e-reputation
https://github.com/ungeimer/TextTagger-Docker
Active learning in annotating micro-blogs dealing with e-reputation
https://github.com/untrix/im2latex
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.