Business License Image Verification with Machine Learning and OCR
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Solution Overview
Problem
The manual verification of business licenses during account sign-up processes in service provider networks is labor-intensive, time-consuming, prone to errors, and frustrating for users due to the need for manual entry and validation of company information, leading to increased load on validators and potential rejection of applications.
Innovation Solution
Implementing a business verification service within a service provider network that uses machine learning models and optical character recognition (OCR) to automatically process and validate business license images, extracting feature vectors, performing similarity and symbol recognition, and populating account templates with validated information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of business licenses is performed, then validation accuracy can be maintained, but processing time increases significantly and labor costs increase
Solution Approach 1:
The patent replaces the manual mechanical verification process with an automated optical character recognition (OCR) system combined with machine learning models. The OCR service extracts text from uploaded business license images, and the validation service compares this extracted information against government databases and internal records, eliminating the need for human validators while maintaining high accuracy through automated verification algorithms.
Solution Approach 2:
The patent introduces an intermediary validation service that acts as a bridge between the uploaded business license images and the final verification result. This service coordinates multiple components including OCR text extraction, image analysis, database queries, and comparison logic to automatically validate business licenses without direct human intervention, thus reducing processing time while preserving accuracy.
2Reliability
If manual verification of business licenses is performed, then thorough validation can be achieved, but the load on validators increases and scalability is limited
Solution Approach 1:
The patent replaces the human validator system with an automated validation service that can process multiple business license images simultaneously. The system uses OCR technology to extract text from images, validates the extracted information against government databases, and performs cross-verification with internal records, achieving thorough validation without being constrained by human capacity limitations.
Solution Approach 2:
The validation service is designed to handle multiple types of verification tasks simultaneously - extracting text from images, validating against government databases, checking internal records, and detecting fraudulent documents. This multi-functional automated system can process diverse validation requirements through a single unified service, greatly increasing throughput while maintaining reliability.
3Measurement precision
If users manually enter company information during signup, then data accuracy can be controlled, but user frustration increases and entry errors occur
Solution Approach 1:
The patent performs preliminary extraction of company information from the uploaded business license image using OCR technology before the user completes the signup form. The validation service automatically extracts text such as company name, registration number, and address from the image, pre-fills these fields in the signup form, and allows users to review and confirm the extracted information, thereby reducing manual entry while maintaining data accuracy.
Solution Approach 2:
The system provides feedback to users by displaying the extracted information from their uploaded business license image and allowing them to verify its accuracy before submission. This feedback mechanism enables users to correct any extraction errors while minimizing manual entry, thus maintaining data accuracy while significantly improving user convenience.
Data Source
AI summary
This disclosure describes a verification service within a service provider network for automatically verifying and validating documents. A user may upload a document image to the verification service. A pre-processing service may pre-process the document image. The pre-processed document image may then be forwarded to a first machine learning ML model for similarity evaluation. Once the first ML model has completed its evaluation of the document image, the first ML model may forward the document image to a second ML model for symbol recognition, which may then forward the business license to an optical recognition (OCR) service for OCR validation. If the document image is validated, e.g., is an image of a purported document type, as will be discussed further herein, the publishing service may pre-populate, e.g., publish, information from the document image to an account template.