Neural Network Handwritten Character Recognition on Checks

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current automated processes in banks are unable to detect handwritten payees on scanned checks, requiring users to click on the check image to view transaction details, which is inconvenient.

Innovation Solution

A system and method using a neural network classification model to identify handwritten characters on images by reading and converting typed text, locating handwritten text, generating separate cluster images for each character, and classifying them using machine learning to determine the character.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated processes are used to process checks, then processing efficiency is improved, but handwritten payee detection capability deteriorates

Engineering Contradiction:

Improvecheck processing efficiencyVSAvoidhandwritten payee detection

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The system segments the check image processing into distinct stages: OCR for typed text, cluster generation for handwritten text identification, and neural network classification for character recognition. This segmentation allows specialized processing for each text type, enabling automated handling while maintaining handwritten payee detection capability

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediate structures including pixel cluster maps that bridge the gap between raw handwritten text and character classification. These clusters serve as intermediaries that translate visual handwritten patterns into structured data that can be processed by classification algorithms, enabling automated detection of handwritten payees

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If simple automated display is used, then system complexity is reduced, but user convenience deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoiduser convenience

Core Design Contradiction:

Device complexityVSEase of operation

Solution Approach 1:

The system performs self-service by automatically detecting and extracting handwritten payee information without requiring user intervention. The neural network classification and cluster analysis operate autonomously to populate transaction details, eliminating the need for users to manually click on check images to view payee information

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12106591B2Reading and recognizing handwritten characters to identify names using neural network techniques

Publication Date: 2024.10.01 TRUIST BANK

AI summary

A system and method for identifying handwritten characters on an image using a classification model that employs a neural network. The system includes a computer having a processor and a memory device that stores data and executable code that, when executed, causes the processor to read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image; identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image; identify clusters of non-white pixels in the image at the location having the handwritten text; generate an individual and separate cluster image for each identified cluster; classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and determine what character each cluster image is based on the classification.