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/cleanlab/cleanlab
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators
https://github.com/cnguyen10/few_shot_meta_learning
https://github.com/code-implementation1/Code3/tree/main/essay-recogination
https://github.com/code-implementation1/Code6/tree/main/PAGENet
PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition
https://github.com/code-implementation1/Code6/tree/main/psenet
Shape Robust Text Detection with Progressive Scale Expansion Network
https://github.com/code-implementation1/Code9/tree/main/OCRNet
https://github.com/code-implementation1/Code9/tree/main/cflownets
https://github.com/code-implementation1/Code9/tree/main/gflowcausal
https://github.com/consequentai/fneval
https://github.com/courao/ocr.pytorch
https://github.com/cui0523/Code6/tree/main/atae_lstm
https://github.com/cwig/start_follow_read
https://github.com/cxjdavin/active-causal-structure-learning-with-advice
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
https://github.com/cydal/LayoutML_pytorch
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
https://github.com/d-gurgurov/im2latex
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/da03/Attention-OCR
https://github.com/damienlancry/DBAL
https://github.com/debabrota-basu/marich
https://github.com/decile-team/cords
https://github.com/deep-spin/sigmorphon-seq2seq
https://github.com/deepopinion/anls-star-metric
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
https://github.com/deepopinion/anls_star_metric
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
https://github.com/dengdan/seglink
https://github.com/dflemin3/approxposterior
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
https://github.com/dhaalves/CEAL_keras
Cost-Effective Active Learning for Deep Image Classification
https://github.com/dipu-bd/craft-moran-ocr
A Multi-Object Rectified Attention Network for Scene Text Recognition
https://github.com/disooqi/DNN
https://github.com/disooqi/Natasy
https://github.com/ds4sd/deepsearch-toolkit
Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness
https://github.com/englhardt/OneClassActiveLearning.jl
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
https://github.com/englhardt/SVDD.jl
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
https://github.com/esa/AnomalyMatch
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
https://github.com/eugene-yang/tarexp
https://github.com/euphoria0-0/Learning-Loss-for-Active-Learning
https://github.com/facebookresearch/SparseConvNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.