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Document AI

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

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

LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding

https://github.com/qwenlm/qwen2-vl

Qwen2.5-VL Technical Report

https://github.com/qwenlm/qwen2.5-vl

Qwen2.5-VL Technical Report

https://github.com/rubenpt91/pfl-docvqa-competition

Privacy-Aware Document Visual Question Answering

https://github.com/sangeetNSK/ChargridTF-GPU

Chargrid: Towards Understanding 2D Documents

https://github.com/sciencefictionlab/chargrid-pytorch

Chargrid: Towards Understanding 2D Documents

https://github.com/shabie/docformer

DocFormer: End-to-End Transformer for Document Understanding

https://github.com/thanhhau097/chargrid2d

Chargrid: Towards Understanding 2D Documents

https://github.com/thibaultvt/Diard

DiT: Self-supervised Pre-training for Document Image Transformer

https://github.com/uakarsh/TiLT-Implementation

Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer

https://github.com/uakarsh/docformerv2

DocFormerv2: Local Features for Document Understanding

https://github.com/yangyucheng000/papercode-2/tree/main/layout-diffusion-mindspore

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

https://github.com/zinzinhust96/Chargrid-Tensorflow

Chargrid: Towards Understanding 2D Documents

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.