We have identified a few risks that could jeopardize the success of the project. Some of these challenges include optimizing the speed of data transfer and ML model (because we need the user to receive feedback in real-time), dealing with transcribing/reading math symbols and equations, performing extraction on poor-quality images, and making sure that the power consumption of both the device and the phone are appropriate for the device to be useful to user throughout the school day.
We have come up with some risk management plans, which include potentially switching to a smaller-size NN to reduce latency, including a wide variety of training images (which have mathematical symbols, low-contrast, blurry, etc.), making sure we have a high-quality camera to capture images, and switching out components for more power-friendly alternatives if needed.
The major change we made was narrowing the scope of the project to only perform text extraction and reading on presentations in classroom settings. This change was necessary since gathering enough training data for the system to perform recognition on any setting would have been difficult, and we wanted to prioritize the accuracy of our system. The change did not incur any costs in particular, but if time allows, we may expand our system to perform scene description on images included in a presentation.
Here is our most recent schedule:
Here is our most recent block diagram:
For our project, we had to consider multiple ethical concerns in order to make sure the product was as accessible to the public as possible. One specific concern we addressed was welfare: we wanted our product to be comfortable for the user, and we didn’t want it to detract from their overall learning experience, so we are planning to develop our device to be as lightweight and convenient as possible. Welfare concerns are extremely important to consider whenever developing a product, because we want to make sure that the user is as comfortable as possible, and we must make sure that it is accessible to everyone: a product might be convenient for some people, while not so much for others. Both of these kinds of people are valid users, and a product should be developed with both kinds in mind so that as many people as possible can benefit from the device.