Key highlights
Over 99 percent
accuracy
Over 20 percent
error reduction
Over 60 percent
efficiency gain
Challenges
1.
Manually entered meter readings took time and often contained errors.
2.
Data inconsistencies arose due to variability in manual readings.
3.
Verifying and validating meter details manually led to frequent errors and delays.
4.
Users struggled to submit meter readings due to limited platform availability.
5.
Securing meter data transmission and storage was a challenge.
Solution
1.
Users uploaded meter images captured from any camera through the application or web interface.
2.
The system enhanced images by reducing noise, adjusting contrast, and cropping before extracting meter-reading values using advanced optical character recognition (OCR) algorithms.
3.
The solution extracted metadata, including the meter's model number and manufacturer details, and matched them against a pre-existing database to verify and validate the meter type and specifications.
Impact
1.
Reduced manual data entry errors: Automated the process to eliminate human errors in reading and recording meter values.
2.
Reduced data processing time: Accelerated data collection and processing, improving efficiency for users and service providers.
3.
Ensured data consistency: Used OCR and database matching to maintain accuracy.
4.
Enhanced verification and validation: Automatically verified and validated meter details against a database, reducing manual checks.
5.
Improved accessibility: Enabled users to upload images through multiple platforms, including mobile apps and web interfaces.