Deep Learning Address Recognition for Mail Sorting

The patent segments the address recognition task into multiple hierarchical levels: capturing full address images, extracting individual address components (street, city, state, ZIP code), and processing each component separately through specialized neural networks. This segmentation allows parallel processing of different address elements, significantly reducing overall processing time while maintaining accuracy through component-specific recognition models.

The patent replaces the traditional OCR mechanical character-by-character recognition system with a deep learning-based image recognition system. Instead of using OCR to sequentially decode characters, the system uses trained neural networks to directly recognize and classify address components from images, achieving both faster processing and maintained accuracy through pattern recognition rather than mechanical character analysis.

The patent implements preliminary action by pre-training specialized neural networks for each address component recognition task before actual sorting operations. The system captures and stores training images of address components in advance, trains the recognition models on this pre-prepared data, and then uses the trained models for rapid during-sorting recognition. This preliminary preparation enables fast processing during actual sorting without compromising extraction reliability.

The patent introduces dynamics by implementing a flexible, adaptive recognition system that can handle varying address formats, fonts, sizes, and orientations. The deep learning models dynamically adjust to different address presentations through training on diverse samples, allowing the system to maintain high sorting speed while reliably extracting delivery information from varied real-world address formats.