Meter Text Detection Using Adaptive Image Capture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The utility industry faces issues with fraud and accuracy in meter reading, whether customers self-report or professional meter-readers are used, as existing methods lack effective techniques for ensuring the reliability and precision of meter readings.
Innovation Solution
A meter text detection and recognition system that uses image processing and optical character recognition (OCR) techniques, combined with user guidance and fraud detection models, to assess image quality, adjust camera settings, and verify meter readings, thereby enhancing the accuracy and security of utility meter readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual meter reading methods are used (customer self-reporting or professional meter-readers), then the process is simple to operate, but the accuracy and reliability of readings deteriorate due to fraud and human error
Solution Approach 1:
The patent replaces manual mechanical meter reading processes with an automated optical character recognition (OCR) system using mobile device cameras and image processing algorithms. This substitution eliminates human intervention in the reading process, thereby preventing fraud and human error while maintaining operational simplicity through automated image capture and text recognition.
Solution Approach 2:
The patent creates a digital copy of the meter display through camera imaging, then processes this copy using OCR technology to extract the reading. This copying approach allows for automated analysis without physical contact with the meter, improving accuracy while keeping the system simple to deploy using existing mobile devices.
2Reliability
If automated image processing and OCR algorithms are implemented, then measurement precision improves through automated reading capture, but device complexity increases due to multiple processing steps including image quality assessment, camera setting adjustment, and fraud detection
Solution Approach 1:
The patent performs preliminary actions by assessing image quality and automatically adjusting camera settings before capturing the meter reading. This preliminary optimization ensures that the captured image meets the quality requirements for accurate OCR processing, thereby improving reading reliability while automating what would otherwise be complex manual adjustments.
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates image quality metrics and automatically adjusts camera parameters based on this feedback. Additionally, fraud detection algorithms provide feedback to verify the authenticity of captured readings, enhancing reliability through automated validation loops that reduce the need for complex manual verification procedures.
3Manufacturing precision
If image quality assessment and camera setting adjustment are performed, then manufacturing precision of the captured image improves, but loss of time increases due to multiple adjustment and capture steps
Solution Approach 1:
The patent performs preliminary assessment of image quality requirements and pre-adjusts camera settings before the actual capture process. By preparing the optimal capture configuration in advance, the system minimizes the number of adjustment iterations needed during the actual reading process, thereby improving image quality while reducing the total time required.
Solution Approach 2:
The patent applies partial optimization by focusing adjustment efforts only on the critical parameters that most significantly impact OCR accuracy, rather than optimizing all possible camera settings. This selective approach achieves sufficient image quality for reliable reading extraction while minimizing the time spent on adjustments.
Data Source
AI summary
Techniques for meter text detection and recognition are described herein. In an example, an application receives a first image, depicting information displayed by the meter, from an imaging device. One or more qualities of the first image may be assessed, such as focus or lighting. A setting of the imaging device may be adjusted. The adjusting may be based at least in part on the assessed quality of the first image and one or more characteristics of an optical character recognition (OCR) algorithm. Accordingly, the settings of the image-capture device are tuned to the needs of the OCR algorithm. A second image may be captured, depicting information displayed by the meter, using the imaging device adjusted according to the adjusted setting. The OCR algorithm may be applied to the second image to obtain an alphanumeric value associated with the second image. The alphanumeric value is obtained from the OCR algorithm.