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The challenge with developing a solution was that utility meters are often in hard to reach places and subject to brutal weather conditions. These often make the readings hard to read for even the human eye. The solution had to be robust in reading partial digits and blurry readings. The existing procedure would involve a large number of human operators for Quality Assurance, resulting in high costs.
The client had an existing system where Special Meter Readers would do monthly cycles of collecting readings from customer utility meters, the procedure would involve taking a picture of the meter along with a manual reading being recorded. The Meter Reader would then submit both the reading and the image to the server. A unit of Quality Assurance individuals would then manually compare the recorded readings against the readings shown in the image, in case of any discrepancy the Meter Reader would be notified. This QA process was very tedious, expensive and time consuming. To come up with an optimized solution, ByteCorp conducted an R&D cycle to create Deep Learning model that would record the readings from the collected image at runtime and compare with the manually entered readings, in case of a match the QA team would not have to manually screen it, saving time and money.
ByteCorp's approach was to use the clients existing images and apply image enhancement techniques to improve the visibility of the readings in the images, and further passing it to sophisticated state of the art Artificial Intelligence Algorithms that would give the final reading as output, to cut down deployment and implementation costs, the algorithms were deployed of edge in a mobile application.
The most important benefits of the solution were the cutting of costs due to the reduction in the size of the QA team, along with a faster billing cycle for customers as QA was more efficient.
ByteCorp worked on developing the technology by itself after acquiring the dataset from the client, furthermore bested other vendor competition by a huge margin in terms of accuracy and speed.
Let's walk you through the project and explore how we could create something similar for your business.
Prefer email? Head of Growth
Automated Meter Reading is a solution that allows a user to automatically capture the reading of a utility meter using a smartphone camera on the edge.
Let's walk you through the project and explore how we could create something similar for your business.
Prefer email? Head of Growth
The challenge with developing a solution was that utility meters are often in hard to reach places and subject to brutal weather conditions. These often make the readings hard to read for even the human eye. The solution had to be robust in reading partial digits and blurry readings. The existing procedure would involve a large number of human operators for Quality Assurance, resulting in high costs.
The client had an existing system where Special Meter Readers would do monthly cycles of collecting readings from customer utility meters, the procedure would involve taking a picture of the meter along with a manual reading being recorded. The Meter Reader would then submit both the reading and the image to the server. A unit of Quality Assurance individuals would then manually compare the recorded readings against the readings shown in the image, in case of any discrepancy the Meter Reader would be notified. This QA process was very tedious, expensive and time consuming. To come up with an optimized solution, ByteCorp conducted an R&D cycle to create Deep Learning model that would record the readings from the collected image at runtime and compare with the manually entered readings, in case of a match the QA team would not have to manually screen it, saving time and money.
ByteCorp's approach was to use the clients existing images and apply image enhancement techniques to improve the visibility of the readings in the images, and further passing it to sophisticated state of the art Artificial Intelligence Algorithms that would give the final reading as output, to cut down deployment and implementation costs, the algorithms were deployed of edge in a mobile application.
The most important benefits of the solution were the cutting of costs due to the reduction in the size of the QA team, along with a faster billing cycle for customers as QA was more efficient.
ByteCorp worked on developing the technology by itself after acquiring the dataset from the client, furthermore bested other vendor competition by a huge margin in terms of accuracy and speed.
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