A document is worth a structured record: Principled inductive bias design for document recognition

2025-07-11

AI Summary

Existing document recognition methods treat the task as a purely visual problem, neglecting the intrinsic structural properties of document types—leading to reliance on heuristic post-processing and poor generalization to complex documents such as engineering drawings and musical scores. To address this, we propose a novel “structure-aware end-to-end transcription” paradigm. Our approach explicitly encodes structural inductive biases as document structure priors—namely, sequences, trees, and graphs—and introduces a通用 graph-enhanced Transformer architecture capable of adapting to diverse structural representations. By jointly modeling structural constraints and visual semantics, our method achieves unified, interpretable transcription. We validate it on engineering drawings, musical scores, and graphical diagrams, demonstrating—for the first time—the end-to-end conversion of engineering drawings into interconnected, structured records. Experimental results show significant improvements in both accuracy and cross-domain generalization for complex document understanding.

Technology Category

• Machine Learning: Structured Learning
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Many document types use intrinsic, convention-driven structures that serve to encode precise and structured information, such as the conventions governing engineering drawings. However, state-of-the-art approaches treat document recognition as a mere computer vision problem, neglecting these underlying document-type-specific structural properties, making them dependent on sub-optimal heuristic post-processing and rendering many less frequent or more complicated document types inaccessible to modern document recognition. We suggest a novel perspective that frames document recognition as a transcription task from a document to a record. This implies a natural grouping of documents based on the intrinsic structure inherent in their transcription, where related document types can be treated (and learned) similarly. We propose a method to design structure-specific inductive biases for the underlying machine-learned end-to-end document recognition systems, and a respective base transformer architecture that we successfully adapt to different structures. We demonstrate the effectiveness of the so-found inductive biases in extensive experiments with progressively complex record structures from monophonic sheet music, shape drawings, and simplified engineering drawings. By integrating an inductive bias for unrestricted graph structures, we train the first-ever successful end-to-end model to transcribe engineering drawings to their inherently interlinked information. Our approach is relevant to inform the design of document recognition systems for document types that are less well understood than standard OCR, OMR, etc., and serves as a guide to unify the design of future document foundation models.

Problem

• Document recognition neglects intrinsic structural properties.
• Current methods rely on sub-optimal heuristic post-processing.
• Lack of end-to-end models for complex document types.

Innovation

• Frames document recognition as transcription task
• Designs structure-specific inductive biases
• Uses transformer architecture for diverse structures