Document Splitting Neural Network for Auto-Classification

The document processing system is divided into multiple independent modules: visual splitting neural network for page segmentation, optical character recognition subsystem for text extraction, title classifier for document type identification, and grouper subsystem for organizing sub-documents. Each module handles a specific processing task independently, enabling parallel execution and improving overall processing speed while maintaining manageable complexity through modular architecture.

The patent replaces manual mechanical document processing with automated machine learning-based systems. The visual splitting neural network automatically detects and segments document pages, the optical character recognition subsystem extracts text content, the title classifier identifies document types, and the grouper subsystem organizes information - all replacing manual operations with intelligent automated processes that significantly increase productivity.

The system performs self-service document processing by automatically classifying documents without human intervention. The title classifier and document classifier work together to automatically identify document types and extract relevant information, with the system making decisions based on learned patterns from training data. This automated self-service approach maintains high classification accuracy while dramatically reducing processing time and eliminating labor intensity.

The system incorporates feedback mechanisms where the title classifier generates confidence scores for identified titles, and the document classifier uses these scores to determine document types. The grouper subsystem receives feedback about document characteristics and adjusts grouping decisions accordingly. This iterative feedback process ensures accurate classification while operating at machine speed, resolving the contradiction between accuracy and time consumption.

The system performs partial processing actions with high confidence and reserves the ability to handle edge cases. The optical character recognition subsystem extracts text with confidence scores, and the title classifier only finalizes document type identification when confidence exceeds thresholds. This partial action approach maintains reliability by not making premature decisions, while still achieving high processing speed through automated batch processing of clear-cut documents.

The system prepares for potential errors by incorporating multiple validation layers before final processing decisions. The visual splitting neural network pre-segments pages, the optical character recognition subsystem pre-extracts text with confidence metrics, and the title classifier pre-identifies potential document types before the grouper subsystem finalizes organization. This beforehand cushioning of validation ensures reliability by catching errors early in the process while maintaining high throughput.

The system achieves universality by implementing a multi-functional architecture where the visual splitting neural network handles page segmentation for all document types, the optical character recognition subsystem extracts text from various formats, the title classifier identifies different document types, and the grouper subsystem organizes them accordingly. This universal approach enables the system to handle diverse document formats without increasing individual component complexity, as each component is designed to be broadly applicable.

The system manages format variability by dynamically adjusting processing parameters based on detected document characteristics. The optical character recognition subsystem adapts its extraction parameters based on document layout, the title classifier adjusts its classification thresholds based on document type, and the grouper subsystem modifies grouping criteria based on identified patterns. This parameter adaptation enables high versatility while keeping detection complexity manageable by using learned patterns rather than complex hard-coded rules.