Document AI: Advanced Layout Analysis & Multimodal Understanding

This repository showcases a series of advanced projects in Document AI, focusing on document layout analysis, multimodal grounding, and high-fidelity segmentation. Each notebook is designed for experimentation and can be launched directly in Google Colab.

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🚀 Key Features

• Document Layout Analysis: Leveraging LayoutLMv3 and UDOP for structural understanding.
• Segmentation: Utilizing the Segment Anything Model (SAM) for granular document element identification.
• Multimodal Grounding: Fine-tuning KOSMOS-2 for vision-language tasks.
• Hugging Face Integration: Built primarily with the transformers and datasets libraries.

📊 Datasets & Models

Component	Description
Datasets	Primarily DocLayNet
Models	LayoutLMv3, KOSMOS-2, SAM, UDOP

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📓 Notebooks

Project	Description	Link
LayoutLMv3 Fine-tuning	Fine-tune LayoutLMv3 on DocLayNet using the HF Trainer.
KOSMOS-2 Grounding	Instruction tuning for multimodal grounding tasks.
LayoutLMv3 Inference	Rapid inference and visualization script for LayoutLMv3.
SAM Segmentation	Apply the Segment Anything Model to complex documents.
UDOP Encoder Tuning	Fine-tune the Universal Document Processing encoder.
UDOP Inference	Inference and structural analysis using UDOP.

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🛠️ Requirements

Typical requirements across these notebooks include:

• transformers
• datasets
• torch
• pillow
• accelerate

Each notebook contains its own dependency installation cell for convenience.