Zero-footprint Image Capture via Neural Network Frame Selection
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Solution Overview
Problem
Current mobile device image capture methods require significant user intervention, such as manual lighting adjustment and stabilization, leading to low-quality images and user dissatisfaction due to the need for additional application installations.
Innovation Solution
An automated image capture system using a neural network to analyze video streams from a mobile device's camera, selecting suitable frames based on quality metrics and performing optical character recognition without the need for pre-installed applications, utilizing a zero-footprint application that is downloaded and deleted after use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated image capture is implemented using a neural network to analyze video streams, then image quality and user convenience are improved, but device complexity and processing requirements increase
Solution Approach 1:
The system performs automated document capture by having the mobile device itself execute the capture, analysis, and processing functions through integrated neural networks and quality metric evaluation, eliminating the need for external specialized equipment or manual intervention
Solution Approach 2:
The mobile device is designed to perform multiple functions including video capture, neural network inference, quality metric evaluation, and document processing within a single integrated system, rather than requiring separate specialized devices for each function
2Manufacturing precision
If multiple hypotheses and quality metrics are evaluated to select optimal frames, then image quality is improved, but processing time and computational resources increase
Solution Approach 1:
The system generates multiple hypotheses defining image borders and evaluates quality metrics for each, but selectively processes only the most promising candidates based on preliminary assessments, rather than exhaustively analyzing every possible frame
Solution Approach 2:
The system dynamically adjusts evaluation parameters and quality thresholds based on the specific characteristics of each video stream and document type, optimizing the balance between processing thoroughness and speed for different capture scenarios
3Ease of manufacture
If zero-footprint application is used for document capture, then ease of deployment is improved, but functionality and reliability may be reduced
Solution Approach 1:
The system extracts and executes only the essential document capture functionality as a temporary zero-footprint application, separating the core capture logic from the full application suite, allowing deployment without permanent installation while maintaining essential reliability
Solution Approach 2:
The system performs preliminary setup and configuration of the neural network models and quality metrics during the initial execution phase, ensuring all necessary components are prepared and validated before actual document capture begins, thereby maintaining reliability despite the temporary nature of the application
Data Source
AI summary
A computer-implemented method for image capture by a mobile device, comprising: receiving, by a video capturing application running on a mobile device, a video stream from a camera of the mobile device; identifying a specific frame of the video stream; generating a plurality of hypotheses defining image borders within the specific frame; selecting, by a neural network, a particular hypothesis among the plurality of hypotheses; producing a candidate image by applying the particular hypothesis to the specific frame; determining a value of a quality metric of the candidate image; determining that the value of the quality metric of the candidate image exceeds one or more values of the quality metric of one or more previously processed images extracted from the video stream; wherein the image capture application is a zero-footprint application.