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/Mephisto405/Learning-Loss-for-Active-Learning
https://github.com/MichalBusta/E2E-MLT
E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text
https://github.com/Mind23-2/MindCode-140
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://github.com/Mind23-2/MindCode-157
https://github.com/Mind23-2/MindCode-35
https://github.com/Mind23-2/MindCode-4/tree/main/gflowcausal
https://github.com/Mind23-2/MindCode-5
https://github.com/Mind23-2/MindCode-5/tree/main/PAGENet
PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition
https://github.com/Mind23-2/MindCode-5/tree/main/psenet
Shape Robust Text Detection with Progressive Scale Expansion Network
https://github.com/MindCode-4/code-12/tree/main/MCA
https://github.com/MindCode-4/code-2/tree/main/pix2struct
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/MindCode-4/code-3/tree/main/deit
DiT: Self-supervised Pre-training for Document Image Transformer
https://github.com/MindCode-4/code-3/tree/main/donut
https://github.com/MindCode-4/code-5/tree/main/table_transformer
TableFormer: Table Structure Understanding with Transformers
https://github.com/MindCode-4/code-5/tree/main/trocr
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/MindSpore-MS-Code2/code0/tree/main/essay-recogination
https://github.com/MindSpore-paper-code-2/code2/tree/main/PAGENet
PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition
https://github.com/MindSpore-paper-code-3/code7/tree/main/essay-recogination
https://github.com/MindSpore-paper-code-3/code8/tree/main/essay-recogination
https://github.com/MindSpore-scientific/code-4/tree/main/Integrated-Gradient
Integrated Gradient Correlation: a Dataset-wise Attribution Method
https://github.com/Mingtzge/2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement
https://github.com/ModelBunker/MORAN-PyTorch
A Multi-Object Rectified Attention Network for Scene Text Recognition
https://github.com/Mohammad-Asad-110/Fashion-Recommendation
Buy Me That Look: An Approach for Recommending Similar Fashion Products
https://github.com/Mushroomcat9998/DBNet
Real-time Scene Text Detection with Differentiable Binarization
https://github.com/Mushroomcat9998/PaddleOCR
https://github.com/Naagar/cornseedsdataset
Automated Seed Quality Testing System using GAN & Active Learning
https://github.com/Narp99/FRI_2018
STN-OCR: A single Neural Network for Text Detection and Text Recognition
https://github.com/Nikolai10/scrabble-gan
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
https://github.com/NormXU/DocParser
DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents
https://github.com/OCR4all/LAREX
https://github.com/OPPO-Mente-Lab/Subject-Diffusion
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
https://github.com/OREL-group/dBV
Continual Developmental Neurosimulation Using Embodied Computational Agents
https://github.com/Olamyy/nass-ai
https://github.com/OneYearIsEnough/PaddleOCR-Recog-LmdbDataset-Conversion
https://github.com/OzHsu23/chineseocr
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.