- The paper introduces Dessurt, an end-to-end Transformer model that integrates text recognition into document understanding, reducing dependency on external OCR systems.
- It demonstrates flexibility across diverse documents by auto-regressively generating text outputs for tasks like classification, question answering, form parsing, and handwriting recognition.
- Dessurt's comprehensive evaluation on multiple datasets highlights its potential to unify document processing and pave the way for future multi-modal applications.
End-to-End Document Recognition and Understanding with Dessurt
The presented paper introduces Dessurt, a novel approach to document understanding using end-to-end Transformers. Unlike traditional methods which rely on separate Optical Character Recognition (OCR) models, Dessurt integrates text recognition directly within its document understanding framework. The architecture permits arbitrary text generation based on the document image and task requirements, offering substantial flexibility across different document domains and tasks. The evaluation of Dessurt spans multiple datasets and tasks, marking it as a comprehensive solution in document analysis.
Key Features of Dessurt
Dessurt's architecture distinguishes itself from previous approaches such as the LayoutLM family by eliminating dependencies on external OCR systems. This integration allows Dessurt to address two significant limitations—restricted output capability and reliance on high-quality OCR results. Dessurt's capabilities are enhanced by its single-pass processing, which seamlessly incorporates text recognition and document understanding. By auto-regressively producing text outputs, Dessurt can generate additional outputs not limited to the input tokens, thus addressing the inherent restrictions in encoder-only approaches.
Moreover, Dessurt is pre-trained on diverse datasets such as IIT-CDIP, synthetic Wikipedia text, synthetic handwriting, and synthetic forms. These datasets collectively prepare Dessurt for a wide array of document tasks including document classification, question answering, form understanding, and handwriting recognition. Additional training tasks are introduced to strengthen Dessurt's capacity in reading and parsing structured documents.
Dessurt's versatility was evaluated across several datasets and tasks, acting as a testament to its broad applicability:
- Document Classification: Dessurt's performance on the RVL-CDIP dataset was corroborated by an accuracy comparable to state-of-the-art models. However, it must be noted that specialized vision-based models might hold an edge due to superior visual feature extraction.
- Question Answering: For the DocVQA dataset, Dessurt showed effectiveness in reading comprehension tasks, although it lagged behind models leveraging strong external text recognition systems. With HW-SQuAD, Dessurt exhibited commendable performance, indicating its robust ability to handle handwritten text.
- Form Understanding: Dessurt demonstrated adaptability in handling modern and historical forms as evidenced in FUNSD and NAF datasets respectively. Despite its flexibility, performance on form parsing tasks indicated room for improvement compared to models optimized specifically for structured document analysis.
- Handwriting Recognition: Dessurt showed favorable results in full-page handwriting recognition, achieving competitive CER and WER compared to specialized models.
- Named Entity Recognition: Dessurt was effective in the IAM NER task, though specialized approaches continued to outperform it due to their strong language modeling capabilities.
Implications and Future Directions
Dessurt represents a significant advancement in document analysis, particularly in its capability to unify text recognition and document understanding into a single framework. This architectural shift inherently reduces complexity, paving the way for broader, more efficient applications in document processing. The ability to adapt to different visual domains and tasks further underscores its potential as a versatile tool in AI-driven document analysis.
The implications for future developments are profound: Dessurt's end-to-end approach could be extended beyond traditional document tasks to complex multi-modal problems where visual and textual information coexist. With continuing improvements in pre-training methodologies and data synthesis, Dessurt and similar architectures might increasingly dominate document analysis applications, offering streamlined and holistic solutions.
Further research might focus on refining model components to balance flexibility and performance. As demonstrated by Dessurt's comparative results, optimizing recognition and language modeling remains pivotal in maximizing efficacy across the diverse range of document tasks. As models evolve to more accurately mimic human-like document understanding, integrating broader datasets and advanced NLP techniques might yield consistently superior outcomes.