https://github.com/kakirastern13/OCR-with-YOLO
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
Papers with Code 아카이브
Optical Character Recognition (OCR)
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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.
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An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
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https://github.com/kurapan/CRNN
https://github.com/kurapan/EAST
https://github.com/kyutai-labs/moshivis
https://github.com/lamps-lab/ETDMiner
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A Benchmark on Uncertainty Quantification for Deep Learning Prognostics
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Attention-based Extraction of Structured Information from Street View Imagery
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Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
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Shape Robust Text Detection with Progressive Scale Expansion Network
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Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
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XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding
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Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
https://github.com/liushuchun/EAST.pytorch
https://github.com/llavar/llavar-2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
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MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
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A Holistic Representation Guided Attention Network for Scene Text Recognition
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Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.