DeepCPCFG: Deep Learning and Context Free Grammars for End-to-End Information Extraction (2103.05908v2)

Abstract: We address the challenge of extracting structured information from business documents without detailed annotations. We propose Deep Conditional Probabilistic Context Free Grammars (DeepCPCFG) to parse two-dimensional complex documents and use Recursive Neural Networks to create an end-to-end system for finding the most probable parse that represents the structured information to be extracted. This system is trained end-to-end with scanned documents as input and only relational-records as labels. The relational-records are extracted from existing databases avoiding the cost of annotating documents by hand. We apply this approach to extract information from scanned invoices achieving state-of-the-art results despite using no hand-annotations.

• The paper DeepCPCFG introduces a novel method integrating deep learning with Conditional Probabilistic Context-Free Grammars for end-to-end information extraction from documents without manual layout annotations.
• DeepCPCFG trains models using only relational database records linked to scanned documents, bypassing the expensive process of detailed image annotation.
• Experiments demonstrate that DeepCPCFG enhances structured parsing performance and exhibits compelling generalization capabilities across diverse document types.

An Analysis of "DeepCPCFG: Deep Learning and Context-Free Grammars for End-to-End Information Extraction"

The paper "DeepCPCFG: Deep Learning and Context-Free Grammars for End-to-End Information Extraction" by Freddy C. Chua and Nigel P. Duffy presents an innovative approach to structure information extraction from complex business documents, particularly those like invoices and receipts that often contain hierarchical and recursive structures. The primary difficulty addressed by this paper is the challenge of extracting structured data from documents without the reliance on manually annotated training data, a process that's often costly and time-consuming.

Overview of DeepCPCFG Methodology

The core contribution of this paper is the Deep Conditional Probabilistic Context-Free Grammars (DeepCPCFG), which integrates Recursive Neural Networks with probabilistic context-free grammars to parse two-dimensional document layouts. This approach is distinct because it focuses on the structural information to be extracted rather than the layout of the document itself. By doing so, the system circumvents the need for the exhaustive layout analysis that characterizes much of the prior work in the area of document intelligence. The paper proposes a grammar-based structured prediction model that decouples information extraction from exact document type specifications.

Key Methodological Innovations:

1. End-to-End Learning without Annotations: DeepCPCFG trains using scanned documents labeled only by relational records from enterprise databases, bypassing the necessity for detailed human annotations of document images.
2. 2D Parsing with CPCFGs: The paper extends the capabilities of Conditional Probabilistic Context-Free Grammars to handle two-dimensional parsing through deep learning models integrated within a CYK-style parsing framework. This approach provides computational feasibility even for parsing documents with complex structures.
3. Structured Representation and Parsing: By employing CFGs to describe the information schema rather than document layout, DeepCPCFG maintains robustness against various document layouts and is adaptable to new document patterns without retraining or re-annotation.

Experimental Validation

Experiments were conducted on a variety of datasets, including proprietary invoices and publicly available datasets such as RVL-CDIP and CORD receipts. Performance was evaluated using metrics suited for structured prediction tasks (Hierarchical Edit-Distance - HED), assessing precision and recall based on the extraction of all relevant information fields.

• Performance Metrics: The structured parsing model, when fine-tuned, demonstrated significant enhancements over conventional baseline methods, especially in recognizing line-item groupings without explicit segmentation or prior annotations.
• Generalization: While pre-trained on diverse document types, the model showed compelling generalization abilities. This was exemplified by applying it to unseen datasets like the RVL-CDIP invoices, thereby underscoring the model's flexibility and broad applicability.

Implications and Future Directions

The implications of this research are multifaceted, suggesting significant practical value in automating document processing tasks across various industries. The end-to-end nature of the system reduces dependency on extensive manual preprocessing, thus decreasing operational costs and increasing adaptability. Theoretically, this contributes to advancements in parsing hierarchical data structures, promoting future research that combines structured prediction techniques and neural network models for broader AI applications.

Future work may explore the integration of more advanced transformer-based models to enhance the semantic understanding of document content further and extend this approach to handle multi-page documents or documents with more complex nested structures. Additionally, incorporating unsupervised learning methods to refine probabilistic grammar representations could potentially enhance model accuracy on noisy data.

In essence, DeepCPCFG offers a promising step towards more efficient, less resource-intensive information extraction from structured documents, presenting both academic interest in parsing methodologies and substantial economic impact potential in business applications.