Welcome to the world of BROS (BERT Relying On Spatiality)! If you’re looking to elevate your document processing capabilities, you’ve landed on the right blog. In this guide, we will explore how BROS utilizes OCR (Optical Character Recognition) results to extract valuable information from documents with precision.

What is BROS?

BROS is a pre-trained language model that focuses on both text and layout, enabling it to extract critical pieces of information from documents effectively. Think of it as a skilled librarian who not only reads the text but also understands the best way to find the most relevant information based on where it’s located on the page.

Key Features of BROS

• Extracts ordered item lists from receipts.
• Utilizes OCR results comprised of text and bounding box pairs.
• Pre-trained to enhance key information extraction tasks.

How to Get Started with BROS

To harness the power of BROS, follow these simple steps:

• Train your Model: Start by selecting one of the pre-trained models available.
• Input the OCR Results: Provide the text and bounding box pairs from the document images.
• Information Extraction: Utilize BROS to extract the desired key information.

Pre-Trained Models Overview

Here’s a quick summary of the pre-trained models you can use:

 Model Name               # Params                            Hugging Face - Models
------------------------------------------------------------------------
bros-base-uncased        110M                               [naver-clova-ocr/bros-base-uncased](https://huggingface.co/naver-clova-ocr/bros-base-uncased)
bros-large-uncased      340M                               [naver-clova-ocr/bros-large-uncased](https://huggingface.co/naver-clova-ocr/bros-large-uncased)

Understanding the Code: An Analogy

Imagine you’re at a restaurant, trying to place an order using a menu that has both pictures and descriptions of the dishes. Each dish corresponds to a specific item on the menu, similar to how text and bounding box pairs represent the OCR results in a document.

Here’s how selecting a model is akin to choosing a dish:

• The bro-base-uncased model is like the “small plate” option, offering you a modest choice for lighter tasks, perfect for initial trials.
• The bros-large-uncased model is your “full-course meal,” ideal for demanding tasks requiring in-depth extraction capabilities.

Troubleshooting

If you encounter issues while using BROS, consider the following troubleshooting tips:

• Ensure that your OCR results are accurate and complete. Incomplete texts may hinder extraction quality.
• Double-check model compatibility with your data type; mismatched inputs can lead to erroneous outputs.
• Experiment with different pre-trained models to find the one best suited for your specific task.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Now that you are familiar with BROS, dive in and begin the journey of extracting meaningful insights from your documents effortlessly!