Papers with Code 아카이브
Key-value Pair Extraction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 20
Extract key-value pairs from a form-like document.
A prediction is considered TP if the predicted key and value contents match the ground truths. Key-value pair F1-score is employed as the metric.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alibabaresearch/advancedliteratemachinery
공식 코드GeoLayoutLM: Geometric Pre-training for Visual Information Extraction
https://github.com/chongzhangfdu/tpp
공식 코드Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
https://github.com/clovaai/donut
공식 코드https://github.com/ibm/kvp10k
공식 코드KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents
https://github.com/jpwang/lilt
공식 코드https://github.com/microsoft/unilm
공식 코드LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/microsoft/unilm/tree/master/layoutlmv3
공식 코드LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/microsoft/unilm/tree/master/layoutxlm
공식 코드LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
https://github.com/2023-MindSpore-1/ms-code-2/tree/main/OCRNet
https://github.com/2024-MindSpore-1/Code3/tree/main/VI-LayoutXLM
LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
https://github.com/MS-P3/code3/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MS-P3/code3/tree/main/lilt
https://github.com/MindCode-4/code-3/tree/main/donut
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/MindSpore-scientific-2/code-14/tree/main/lilt
https://github.com/MindSpore-scientific/code-7/tree/main/LayoutLMv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/layoutxlm
LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
https://github.com/PaddlePaddle/PaddleOCR
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/WinterShiver/Token-Path-Prediction
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
https://github.com/code-implementation1/Code9/tree/main/OCRNet
https://github.com/facebookresearch/data2vec_vision
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/huggingface/transformers
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/impira/docquery
https://github.com/pwc-1/Paper-9/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/pwc-1/Paper-9/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/pwc-1/Paper-9/tree/main/lilt
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.