https://github.com/zrrskywalker/llama-adapter
공식 코드LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
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/zwt233/Grain
공식 코드Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization
https://github.com/zwt233/igp
공식 코드Information Gain Propagation: a new way to Graph Active Learning with Soft Labels
https://github.com/zwt233/rim
공식 코드https://github.com/zzsfornlp/zmsp
공식 코드https://github.com/zzzhang-jx/marior
공식 코드Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild
https://gitlab.com/akhilsnair/workflow_sisso
공식 코드https://gitlab.com/asolovykh/mlip-i-pi-tutorial
공식 코드Path-integral molecular dynamics with actively-trained and universal machine learning force fields
https://gitlab.com/dprl/graphics-extraction
공식 코드ChemScraper: Leveraging PDF Graphics Instructions for Molecular Diagram Parsing
https://gitlab.com/gortizji/product_graphs
공식 코드https://gitlab.com/haghdam/deep_active_learning
공식 코드https://gitlab.com/hylleraasplatform/hyal
공식 코드Learning atomic forces from uncertainty-calibrated adversarial attacks
https://gitlab.com/jochre/jochre3-ocr
공식 코드https://gitlab.com/jochre/jochre3-search
공식 코드https://gitlab.com/serralba/concept_drift
공식 코드Efficient Concept Drift Handling for Batch Android Malware Detection Models
https://gitlab.com/zaverkin_v/gmnn
공식 코드https://gitlab.is.tue.mpg.de/mbuissonfenet/active_learning_gp
공식 코드https://worksheets.codalab.org/worksheets/0x39ba5559790b4099a7ff75f916ce19a4
공식 코드On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks
https://worksheets.codalab.org/worksheets/0x8ef22fd3cd384029bf1d1cae5b268f2d
공식 코드On the Relationship between Data Efficiency and Error for Uncertainty Sampling
https://worksheets.codalab.org/worksheets/0x978bd1ba6b284678994c067f9c9bdb78
공식 코드https://worksheets.codalab.org/worksheets/0xf8dfe5bcc1dc408fb54b3cc15a5abce8
공식 코드Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
https://zenodo.org/record/8249818
공식 코드https://github.com/18520339/dbnet-tf2
Real-time Scene Text Detection with Differentiable Binarization
https://github.com/1ytic/warp-rnnt
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/LSTM
https://github.com/2023-MindSpore-1/ms-code-2/tree/main/OCRNet
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/cnnctc
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/essay-recogination
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/psenet
Shape Robust Text Detection with Progressive Scale Expansion Network
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/atae_lstm
https://github.com/2023-MindSpore-1/ms-code-43
Real-time Scene Text Detection with Differentiable Binarization
https://github.com/2023-MindSpore-4/Code3/tree/main/east
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bros
https://github.com/2024-MindSpore-1/Code3/tree/main/CAN
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition
https://github.com/2024-MindSpore-1/Code3/tree/main/PGNet
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
https://github.com/9ruddls3/CRNN_Pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.