End-to-End Document Extraction via Fully Convolutional Neural Network

The patent combines OCR and information extraction into a single integrated model that processes documents in one pass. The fully convolutional neural network simultaneously performs character recognition and field extraction, eliminating the sequential two-stage process and achieving both improved speed and consistency.

The patent introduces a custom fully convolutional neural network as an intermediary component between the document image and the final extracted information. This custom model replaces third-party OCR services and provides consistent, optimized processing specifically tailored for tax form documents.

The patent extracts the OCR functionality from external third-party services and implements it locally within a custom fully convolutional neural network. This eliminates the need for Internet data exchange and external service calls, removing runtime delays while maintaining extraction accuracy.

The patent performs all OCR and extraction operations in a single preliminary pass through the fully convolutional neural network, rather than requiring multiple sequential steps. This preliminary action captures both character recognition and field extraction simultaneously, eliminating subsequent processing delays.

The patent applies local quality by designing a specialized fully convolutional neural network tailored specifically for tax form documents. The model's architecture and training data are optimized for the particular structure and fields of tax forms, achieving high precision for this specific document type rather than using a generic solution.

Instead of using a generic OCR system and attempting to adapt it to specific document types, the patent inverts the approach by designing a custom model from the ground up that is inherently optimized for tax forms. This specialized model achieves superior performance for specific extraction tasks.