https://github.com/felixopolka/ggp-tf2
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Papers with Code 아카이브
Optical Character Recognition (OCR)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/fh2019ustc/docgeonet
DocScanner: Robust Document Image Rectification with Progressive Learning
https://github.com/fidelity/mab2rec
https://github.com/fidelity/selective
https://github.com/flowersteam/teachDeepRL
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots
https://github.com/flukeskywalker/highway-networks
https://github.com/foamliu/CRNN
https://github.com/freedombenLiu/ad_examples
https://github.com/frkl/active-learning
Active Learning for Visual Question Answering: An Empirical Study
https://github.com/frotms/PaddleOCR2Pytorch
https://github.com/gauthamkrishna-g/ActiveHARNet
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition
https://github.com/gd-zhang/noisy-K-FAC
https://github.com/genedisco/genedisco-starter
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
https://github.com/gentopia-ai/gentopia
https://github.com/gewu-lab/ogm-ge_cvpr2022
IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling
https://github.com/githubharald/simplehtr
https://github.com/gladisor/TextGenerator
https://github.com/glard/Active-Generative-Design
Active learning based generative design for the discovery of wide bandgap materials
https://github.com/gortizji/product_graphs
https://github.com/gregbugaj/unet-denoiser
Binary Document Image Super Resolution for Improved Readability and OCR Performance
https://github.com/gregory-kyro/chemspaceal
https://github.com/gtn-org/gtn
https://github.com/gwenniger/multi-hare
https://github.com/gxym/textbpn-puls-plus
https://github.com/hackerxiaobai/OpenTag_2019
https://github.com/hadi-gharibi/pytorch-lstm
https://github.com/harish2704/pottan-ocr
https://github.com/hertelm/ocr-postcorrection
https://github.com/hieu28022000/PaddleOCR
https://github.com/hieunghia-pat/ViConsFormer
https://github.com/hillup/active_learning
Active Learning for Convolutional Neural Networks: A Core-Set Approach
https://github.com/huggingface/transformers
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/huizhang0110/AON
https://github.com/huyhoang17/DB_text_minimal
Real-time Scene Text Detection with Differentiable Binarization
https://github.com/ielab/ai-review
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval
https://github.com/ihdia/instance-segmentation-v1
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.