Our project was centred around theme of 'augmentation' along with consideration of the coding challenges 'sustainability'. Our program offers a new method of study to those who are perhaps unable to afford / access conventional education. We developed it during DurHack 2022.
What it does
Our project culminates into a smartphone app that scans (using smartphone cameras) worksheets problem-sheets, textbooks and all other such study media. The scan highlights keywords, topics and points of interest within the text and returns to the user with possible areas to research. For example as seen in the demo pitched to the assessors, the program might scan a maths question and deduce which topic of maths best describes the question - whether that might be arithmetic, algebra, geometry etc. Another example may be using the program to scan a history textbook, which will in-turn highlight key names, places and events and return to the user with information that might be of interest to them. The most important factor of this program, would be that this program helps students to learn and gain a curiosity that inspires learning. This means that the program would not provide the user with a direct answer to a question but only point them in some of the right directions - which turned to out be one of the most challenging part of this project; more difficult than giving the user a direct answer.
How we built it
Challenges we ran into
Accomplishments that we're proud of
We were especially proud of creating the React Native smartphone app that would scan and take photos for our project. We were especially impressed at how quick it was to develop a Python API and train a Python neural network - especially with gigabytes worth of datasets. We are very proud of how we got as a team in this Hackathon and are very happy to submit what we have.
What we learned
What's next for Study Buddy
This prototype currently covers Mathematics, so we would aim to improve the dataset of the neural network to handle other subjects - such as Chemistry, History, English, Music and others. We would also want to optimise our application and its API for security and performance.
Ironically, in the last 5 minutes of the event, Enego figured out how to interface with the neural network, from the python server. Sadly, however, we were unable to integrate this into our project in time.
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