Neural Network Pipeline for Document Image Quality Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The quality of document images uploaded by users significantly impacts the document verification pass rates, leading to increased costs and poor user experiences for service providers due to issues like blur, glare, low resolution, and missing key information.
Innovation Solution
Implementing a neural network (NN) pipeline that includes object detection and image quality assessment models to evaluate the quality of document images in real-time, providing feedback on the presence and clarity of key information fields and suggesting improvements for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If document images are processed by AI systems for verification, then verification accuracy is improved, but processing time increases significantly
Solution Approach 1:
The system performs preliminary quality assessment of document images before they undergo full AI verification processing. By evaluating image quality metrics (resolution, lighting, completeness) in advance, the system identifies and filters out low-quality images that would waste computational resources, allowing high-quality images to proceed directly to verification while reducing overall processing time
2Reliability
If document image quality is low (blur, glare, low resolution), then verification pass rate decreases, but processing costs increase
Solution Approach 1:
The system performs preliminary quality assessment of document images before they undergo full AI verification processing. By evaluating image quality metrics (resolution, lighting, completeness) in advance, the system identifies and filters out low-quality images that would waste computational resources, allowing high-quality images to proceed directly to verification while reducing overall processing time
Solution Approach 2:
The system provides immediate feedback to users about document image quality issues (such as blur, glare, missing fields) and guides them to recapture images. This feedback loop prevents low-quality images from entering the verification pipeline, maintaining high pass rates while avoiding the cost of processing inadequate images
3Ease of operation
If real-time image quality assessment is implemented, then user experience is improved, but system complexity increases
Solution Approach 1:
The system segments the document verification process into distinct stages: quality assessment, verification decision, and feedback provision. By separating quality evaluation from the main verification logic, the system provides real-time user guidance without requiring complex integration across the entire verification pipeline, managing system complexity through modular architecture
Data Source
AI summary
There are provided systems and methods for interpretive and qualitative assessments of document image quality for document image submissions. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users, which may be used to engage in interactions with other users and entities including for electronic transaction processing. When utilizing these services, document verification may be required to verify a document. A user may capture an image of a document, such as a driver's license, and an image quality assessment engine may process the image using a pipeline of neural networks configured to provide interpretive and qualitative assessments. A first neural network may determine key information fields present in the image for the document and a document type for the document in the image. A second neural network may then determine image data quality for the fields in the image.