Neural Network Handwriting Recognition for Physical Mail
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Solution Overview
Problem
Current mail management systems face increased costs and inefficiencies due to the need for human review of handwritten address information on physical mail, which hinders competitiveness and scalability.
Innovation Solution
A computerized method for handwriting recognition (HWR) that scans physical mail, identifies handwritten regions, and uses neural networks trained with historical data to automatically recognize and process sender and recipient addresses, integrating machine learning and OCR techniques to convert handwritten text into digital format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human review is used to process handwritten address information, then accuracy of mail forwarding is improved, but cost and processing time increase
Solution Approach 1:
The system enables self-service by implementing an automated neural network-based handwriting recognition system that processes mail addresses without requiring human intervention. The neural network is trained on historical handwriting data and automatically identifies and extracts sender and recipient information from scanned mail images, making the system self-sufficient in handling handwritten addresses.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computational system. Instead of human operators manually examining and transcribing handwritten addresses, the system uses optical scanning combined with neural network-based image recognition to automatically detect, classify, and extract address information from handwritten text on mail envelopes.
2Productivity
If automated HWR systems are implemented, then productivity and cost-effectiveness are improved, but system complexity increases
Solution Approach 1:
The system applies preliminary action by pre-training the neural network using historical handwriting data stored in databases. The training process is performed in advance using two-stage training: first training on raw handwriting samples, then fine-tuning with labeled address information. This preliminary preparation enables the system to handle production mail processing efficiently without requiring complex real-time decision-making.
Solution Approach 2:
The patent introduces an intermediary layer between scanning and recognition by implementing a multi-stage processing pipeline. The system uses image preprocessing, feature extraction, and intermediate data structures to bridge the gap between raw scan images and final address extraction. This intermediary processing simplifies the overall system architecture by breaking down the complex recognition task into manageable stages.
3Measurement precision
If historical data is used for training, then recognition accuracy is improved, but data processing time increases
Solution Approach 1:
The training process is segmented into distinct stages to improve efficiency. The system implements two-stage training: first training the neural network on raw handwriting images to learn basic character and stroke patterns, then conducting a second stage of training with labeled address data to specialize in extracting sender and recipient information. This segmentation allows the system to process historical data more efficiently by focusing on different aspects in separate training passes.
Data Source
AI summary
In one aspect, a computerized method useful for handwriting recognition (HWR) on physical mail envelopes addressed to a user includes the step of scanning a physical mail item to obtain a digital image of the address-side of the physical mail item. The method includes the step of identifying that at least one of a return address region or a recipient address region of the address-side of the physical mail item. The method includes the step of determining that the at least one of the return address region or a recipient address region comprises a handwritten text. The method includes the step of providing a data store of known senders to the recipient address. The data store of known senders comprises a data store of return address information in a known sender handwriting samples and a data store of receiver address information in the known sender handwriting samples. The method includes the step of providing a data store of a receiver's identity and address. The method includes the step of creating a first training set including the historical data store of known senders. The method includes the step of training the neural network in a first stage using the first training set. The method includes the step of creating a second training set for a second stage of training including the data store of a receiver's identity and address. The method includes the step of training the neural network in a second stage using the second training set. The method includes the step of integrating the neural network into an HWR functionality.