https://github.com/littletomatodonkey/Augment-XY-CUT
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding
Papers with Code 아카이브
document understanding
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Document understanding involves document classification, layout analysis, information extraction, and DocQA.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mayubo2333/mmlongbench-doc
MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations
https://github.com/opendatalab/PDF-Extract-Kit
https://github.com/princeton-nlp/CharXiv
https://github.com/pwc-1/Paper-10
Unifying Vision, Text, and Layout for Universal Document Processing
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/lilt
https://github.com/qwenlm/qwen2-vl
https://github.com/qwenlm/qwen2.5-vl
https://github.com/rubenpt91/pfl-docvqa-competition
https://github.com/sangeetNSK/ChargridTF-GPU
https://github.com/sciencefictionlab/chargrid-pytorch
https://github.com/shabie/docformer
DocFormer: End-to-End Transformer for Document Understanding
https://github.com/thanhhau097/chargrid2d
https://github.com/uakarsh/TiLT-Implementation
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
https://github.com/uakarsh/docformerv2
https://github.com/zinzinhust96/Chargrid-Tensorflow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.