DocFormer: End-to-End Transformer for Document Understanding (2106.11539v2)

Abstract: We present DocFormer -- a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). VDU is a challenging problem which aims to understand documents in their varied formats (forms, receipts etc.) and layouts. In addition, DocFormer is pre-trained in an unsupervised fashion using carefully designed tasks which encourage multi-modal interaction. DocFormer uses text, vision and spatial features and combines them using a novel multi-modal self-attention layer. DocFormer also shares learned spatial embeddings across modalities which makes it easy for the model to correlate text to visual tokens and vice versa. DocFormer is evaluated on 4 different datasets each with strong baselines. DocFormer achieves state-of-the-art results on all of them, sometimes beating models 4x its size (in no. of parameters).

An Overview of DocFormer: A Transformer Approach to Visual Document Understanding

The paper introduces "DocFormer," an innovative multi-modal transformer architecture for Visual Document Understanding (VDU), addressing the challenges associated with understanding documents in varied formats and layouts, such as forms and receipts. The DocFormer model is a significant development in document processing technology, as it aims to integrate text, vision, and spatial features effectively.

Key Features of DocFormer

DocFormer employs a pre-training approach, utilizing a set of meticulously designed tasks that encourage multi-modal interaction, setting a precedent in unsupervised pre-training for VDU. The core innovation in DocFormer is its novel multi-modal self-attention layer, which facilitates the fusion of text, vision, and spatial features. This design choice enhances the model's ability to correlate textual and visual tokens, leveraging shared spatial embeddings across modalities to improve document understanding.

Numerical Results and Performance Evaluation:

The authors evaluate DocFormer on four different datasets characterized by strong baselines. Notably, DocFormer achieves state-of-the-art results across these datasets, occasionally surpassing models that are four times larger in terms of parameter size. Such an achievement highlights the efficiency and effectiveness of the DocFormer architecture in handling complex VDU tasks.

Technical Contributions

The paper highlights several technical contributions, including:

1. Multi-modal Self-Attention Layer: This layer efficiently fuses different modalities, unlocking the potential for better feature correlation and enhanced document understanding.
2. Pre-training Tasks: The introduction of two novel unsupervised tasks—Learning-to-Reconstruct and Multi-Modal Masked LLMing—promotes feature collaboration and enhances the pre-training process.
3. Memory Efficiency: By eschewing bulky object-detection networks typically used for visual feature extraction, DocFormer relies on ResNet50 features and joint spatial embeddings, reducing memory requirements and training complexity.

Implications and Future Directions

The practical implications of DocFormer are broad, with the architectural advancements providing an efficient alternative to existing models for VDU tasks. Theoretical implications suggest that further refinement of multi-modal transformers and their attention mechanisms could significantly impact not only document understanding but also other domains where multi-modal data processing is critical.

Looking towards future developments, the research opens several avenues, such as exploring multi-lingual capabilities and adapting the model to additional document types, including information graphics and web pages. Additionally, the methodologies and insights from DocFormer can influence developments in related fields, advancing the state-of-the-art in artificial intelligence and machine learning.

In conclusion, the research presented in DocFormer signifies a notable step forward for VDU tasks by demonstrating how well-designed multi-modal transformers can lead to efficient and powerful document processing tools. This work will likely serve as a foundation for further innovations and improvements in document understanding technologies.