LayoutXLM is a multimodal pre-trained model for multilingual visually-rich document understanding. It goals to bridge language barriers and improve performance on tasks like information extraction, form completion, and receipt analysis. But what exactly does that mean? Let’s break it down!

First, pre-training. Pre-trained models are trained on a large dataset to learn general language patterns before being fine-tuned for specific tasks. This can significantly improve performance and reduce training time. LayoutXLM is no exception it uses a multimodal approach that combines text, layout, and image information in its pre-training process.

Now the benefits of this model. By jointly learning interactions between text and layout information across scanned document images, LayoutXLM can better understand complex documents with multiple languages and formats. This is especially useful for tasks like receipt analysis or form completion where there may be language barriers or inconsistent formatting.

But wait, it gets even better! The authors of this paper also leverage image features to incorporate words’ visual information into LayoutLM. By doing so, they can improve performance on document-level pre-training tasks like classification and segmentation. This is a significant improvement over traditional text-based models that neglect layout and style information in their pre-training process.

To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training. The results speak for themselves LayoutXLM achieves new state-of-the-art results in several downstream tasks like form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42).

If you’re interested in trying out LayoutXLM for yourself, the code and pre-trained models are publicly available at this URL: [insert link here].