Description
LayoutLM is a powerful multimodal pre-training method designed for visually-rich document understanding and information extraction tasks. It uniquely integrates text, layout, and image information to process documents effectively. This approach has led to state-of-the-art performance on challenging tasks such as form understanding and receipt understanding.
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The model's architecture allows it to capture the intricate relationships between textual content and its visual presentation within a document. This makes it particularly adept at handling documents that rely heavily on formatting, such as invoices, forms, and scanned documents.
LayoutLM has seen several advancements, including LayoutLMv2, which further improved performance on various document AI tasks. A significant extension is LayoutXLM, which brings multilingual support by extending LayoutLM to handle seven languages and introduces the XFUND benchmark for multilingual form understanding. This expansion makes LayoutLM a versatile tool for global document processing needs.
The project provides pre-trained models in different sizes (Base and Large) and configurations (Cased and Uncased), readily available on HuggingFace. This accessibility allows researchers and developers to easily integrate LayoutLM into their projects. The repository also includes fine-tuning code and examples, such as a fine-tuning example on the FUNSD dataset, enabling users to adapt the model to specific downstream tasks.
LayoutLM has demonstrated superior results compared to traditional models like BERT and RoBERTa on benchmark datasets such as SROIE, RVL-CDIP, and FUNSD. The project is actively maintained and has released new datasets like TableBank and DocBank, further contributing to the field of document AI. The code is open-source, fostering collaboration and innovation within the community.
- CategoryAI Models & LLMs
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsWeb
LayoutLM's Core Features
Multimodal pre-training for document understanding
Integrates text, layout, and image information
Achieves state-of-the-art results on document AI tasks
Supports form understanding and receipt understanding
Includes multilingual capabilities with LayoutXLM
Offers pre-trained models in Base and Large sizes
Available in Cased and Uncased versions
Fine-tuning code and examples provided
Supports integration with HuggingFace Transformers library
Released new document understanding datasets (TableBank, DocBank)
Open-source project on GitHub
Getting Started with LayoutLM
Access model: Navigate to the LayoutLM GitHub repository or HuggingFace model hub.
Set up environment: Install necessary libraries, including transformers and PyTorch.
Initialize tokenizer: Load the appropriate tokenizer for the chosen LayoutLM model.
Load pre-trained model: Instantiate the LayoutLM model from HuggingFace.
Fine-tune model: Adapt the pre-trained model to specific downstream tasks using provided scripts.
Integrate via API: Utilize the HuggingFace Transformers library for seamless integration into applications.
Optimise performance: Experiment with different model sizes and fine-tuning strategies for best results.
LayoutLM's Use Cases
- Form Understanding
- Receipt Understanding
- Document Information Extraction
- Multilingual Document Processing
- Invoice Processing
- Table Extraction
