AI and ML in document workflows

How AI and machine learning are revolutionizing document processing

Understand the key technical effects of artificial intelligence and machine learning on the transformation of static documents into intelligent, structured data for efficient information retrieval and knowledge retention.

The foundation of intelligent document processing (IDP)

The rapid increase in digital documents in electronic form (such as PDF and PostScript) has illustrated the need for effective and efficient retrieval and organization of this stored material. IDP meets this challenge by using intelligent techniques to fully automate the capture and understanding of the knowledge contained in documents.1

This process is largely based on a sequence of machine learning (ML) steps:

• Layout analysis: The first step is to identify the physical blocks and structures that make up a document. This can include preprocessing modules that convert drawing instructions into objects, followed by algorithms that group semantically related basic blocks based on white spaces and the background structure.
• Logical structure assignment: After identifying the layout structure, the system assigns the corresponding logical role (or semantic role) to each component. This is key to document understanding and enables a wide range of applications, including hierarchical browsing and component-based querying.

Strengthening systems through tailored learning capabilities

• Multiple Instance Learning (MIP): This approach is used to automatically derive rules for grouping elements (such as words into lines), which is particularly important for complex layouts such as multi-column documents.
• Learning through first-level logic: This technique is necessary to express complex relationships between layout components. It is used to classify the document type (e.g. scientific article, newspaper) and to assign roles to the significant components of this class (e.g. title, author, abstract).
• Incremental learning: To cope with the continuous flow of new material, incremental capabilities are being used to refine existing classification and labeling theories. This ensures that the system remains highly adaptable and improves its performance over time.

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The Deep Dive: From Pixels to Semantic Meaning

IDP's scientific development is focused on moving beyond simple optical character recognition (OCR) to real semantic understanding. This is achieved by creating a complex representation of the document:

• Feature vectors: Elementary blocks (such as words) are first described by feature vectors, which include parameters such as position, height, and width.
• Spatial and topological relationships: To really understand the layout, the system describes the relationships between the blocks. This includes spatial relationships (description of occupied space in relation to other blocks) and topological relationships (such as proximity, overlap, and overlap).
• Automatic correction: Manual corrections by subject matter experts can be logged and used by an incremental learning component to refine classification theories. This ensures that the system can automatically fix layout recognition issues through embedded rules.

This entire process aims to extract the meaningful content — the title, abstract, or specific illustrations — to ultimately categorize the subject of the document.

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Impacts: Efficiency, retrieval, and knowledge retention

The application of IDP and the underlying ML/AI techniques has proven beneficial in various areas, such as managing scientific conferences. The measured predictive accuracy for classifying and understanding document components is high (for example, it reaches 97-98% in experiments to identify titles and abstracts).

Document management is crucial for the dissemination and preservation of knowledge. By automatically identifying the logical structure and extracting significant text, IDP enables:

• Improved retrieval: Searching for and accessing information becomes more effective and efficient because the query targets the structured, semantic role (e.g. “all abstracts”) and not just the raw text.
• Structural applications: The logical structure enables applications such as hierarchical browsing and style translation.

The intensive use of intelligent techniques in IDP is successfully moving away from the impracticable solution of manually creating and maintaining indexes for huge amounts of data, and paves the way for automated and highly adaptable document processing solutions.