Extracting prices of fuel from images of gas stations leveraging Azure, the use of Cognitive Services APIs, including the new Recognize Text API, followed by the Spellcheck API to reduce inaccuracies.
We were inspired by the challenge that we end up facing everyday as regular consumers of gas. We wanted to find a way to find the cheapest and closest gas station near us, where spending so much money on gas wouldn't be as big of a burden as it is today. Furthermore, this also made us think about the situations that the gas station owners live in everyday.
The app takes data from around the area, compares it to the fuel prices and provides the user with a database with the different types and costs of fuels.This would help the sellers decide what prices would make a larger profit and earn a profit.
We built this application using the Microsoft Azure algorithms and used their machine learning algorithms in order to sort through our data. We further wanted to implement maps in our application, so we used the google maps API, Firebase in order to connect our back end and front end, data SQL, and tried to use azure to hist our website.
We ran into problems connecting the front end and back end of the website. We also ran into problems hosting our website on azure and had to work our way around it.
We are proud of our back end program that successfully sorts through the data to build the data sets and provide the user with necessary information. We were able to implement the APIs that azure provided and identify the important elements in pictures successfully.
We are going to work towards further integrating the map system where people can check for the nearest gas station and are provided with information about the prices. The consumers are then able to make cheap and convenient decisions.