Improving Document Understanding with Layout Information

In today’s world, businesses deal with an ever-increasing number of digital documents. This rise in documents, from invoices to reports, creates a need for efficient processing. With smart devices, capturing documents has become easier, but it has also led to various quality issues. Companies must find ways to manage these documents to stay competitive.

Understanding documents is not just about reading the text. It also involves recognizing how the text is arranged and how different parts of the document relate to each other. This layout is crucial for understanding the document's meaning. Recent advancements in technology have made it possible to analyze both the text and the visual structure of documents, enhancing the way we comprehend them.

#The Growth of Document Processing Technologies

The increase in digitized documents means that automated systems must keep pace. There are two main approaches used in document processing:

1. Text Focused Models: These models work primarily with the text extracted from documents. They process this information to understand and respond to user tasks.

2. Multi-Modal Models: These models combine both text and images. They analyze the visual elements alongside the text to provide a more complete understanding.

Currently, there is a challenge in choosing between these two types. Text-centric models can leverage vast amounts of text data, but they often miss out on important layout cues. On the other hand, multi-modal models require extensive training on multiple data types, which might not always be feasible.

#The Purpose of This Study

This study investigates whether pure text-based models can be improved for document-specific tasks by incorporating layout information. We look at different methods to enhance prompts for these models by integrating layout details without needing to retrain them. We use popular models like ChatGPT and an open-source model called Solar to test our ideas.

Our experiments reveal that adding layout information can significantly boost performance in understanding documents. This enhancement can make purely text-based models much more effective in handling tasks that require understanding how information is arranged on a page.

#Context of Document Understanding

To grasp the full context of documents, it is crucial to understand both the text and its layout. Recent developments in technology have fostered significant strides in document image understanding. Important milestones include:

• Larger Datasets: New benchmarks help train models using real-world applications, allowing for better evaluation.

• Self-Supervised Learning: These tasks allow models to learn from unmarked data, improving their understanding without needing extensive human input.

These advances have led to the emergence of large language models (LLMs), which excel at various language-related tasks. However, traditional LLMs tend to operate on plain text, sometimes losing essential layout information.

#Our Approach to Document Understanding

In this work, we propose a new method that focuses on the Verbalization of document layout. Our approach consists of several steps:

1. OCR Extraction: First, we extract text from documents using Optical Character Recognition (OCR). This step helps us get the text and its layout information.

2. Verbalization: Next, we transform the extracted information into a structured text format. This format captures not only the text but also its spatial relationships.

3. Prompt Creation: The verbalized document is then combined with specific prompts that outline the tasks to be performed. This combination allows the model to perform the required tasks based on both the text and its layout.

This pipeline is advantageous because it allows us to use existing LLMs without needing additional training, making the approach efficient and straightforward.

#Challenges in Document Understanding

Document comprehension poses various challenges:

• Layout Errors: OCR systems can sometimes misinterpret layouts, leading to inaccurate positioning of text elements.

• Noisy Data: Poor quality inputs can further complicate the process, affecting the model’s performance.

• Complex Documents: Some documents have intricate layouts that are difficult for models to interpret accurately.

By addressing these issues, our research aims to improve how effectively models understand document content based on their structure.

#Evaluating Our Method

To validate our approach, we conducted experiments using datasets that reflect real-world scenarios. We explored how well our method works across different document types and tasks, including:

• Key Information Extraction: Identifying essential elements like names, dates, and totals from documents.

• Question Answering: Responding to specific queries about the content and layout of documents.

We gathered data from multiple sources, allowing us to assess the effectiveness of our model across a wide variety of tasks.

#Analysis of Results

Our findings indicate that incorporating layout information into prompts can lead to substantial improvements. Specifically, models showed performance gains of up to 15% in accuracy when layout details were included. Our enhancements helped the models handle tasks better, showing that layout plays a crucial role in document understanding.

We compared our results against existing benchmarks and found that our method holds its ground against more complex multi-modal models. This points to the effectiveness of our approach in leveraging existing text-based models while maintaining simplicity and ease of implementation.

#Insights from the Experimentation

Throughout our experimentation, we observed several key points:

• Model Performance Varies: Different models respond differently to layout information. Some models were more adept at using the enriched prompts effectively.

• Quality of Input Matters: The performance of LLMs is heavily influenced by the input quality. Higher quality outputs from OCR lead to better performance in understanding documents.

• Complex Layouts are Challenging: Even with the enhancements, very complex layout designs still pose challenges. The more intricate the document structure, the harder it is for models to interpret effectively.

#Future Directions

The results of our study suggest that there is room for further exploration in this field. Future research could investigate the following aspects:

• Integration of Visual Inputs: Exploring models that can take both visual and textual inputs may lead to even better performance.

• Improving OCR Techniques: Enhancing OCR systems to provide more accurate spatial information could further boost the effectiveness of our approach.

• Scaling Solutions: Examining how our methods can work with multi-page documents is another area that requires attention, especially with larger sets of data.

#Conclusion

Our work has demonstrated that adding layout information to prompts for text-based models can significantly enhance document understanding. This approach offers a practical and efficient way to improve existing LLMs without extensive retraining. The results indicate that even simple modifications can lead to better performance, highlighting the importance of document structure in content comprehension.

By focusing on both text and layout, we can bridge the gap between pure text processing and complex multi-modal understanding. This approach presents a promising avenue for future research and practical applications in the field of document processing.