This week, my focus was looking at existing solutions for braille character classification and investigating the tools I would need for an in-house solution. This would help us get a better idea of how we should allocate our time and effort later in the development phase. I took some time to set up and train the GitHub repository I found last week. However, upon completion, I found that the training data was poorly labeled and, even considering the mislabeled data, it was not able to accurately classify our braille inputs.
Despite this failed experiment, the repository was able to give us a good idea of how fast classification can be once a model, in this case a DNN, is trained. Jay was able to provide me with some sample images of what a cropped braille character would look like after his pre-processing pipeline. Unfortunately, I lost some time this weekend due to illness but I hope to start next week by retraining the model with correct data and testing it against Jay’s inputs. If the pre-written solution turns out to be a dead end, I am looking into the most likely alternative of writing our own featurization techniques using Hough transform etc. and feeding them into OpenCV’s classification pipeline.
This week, I also took some time to design some diagrams for our design review, which will hopefully make it easier to communicate our vision during the presentation. It also helped us as a team to better understand our shared vision before moving into the development and implementation phase.
According to our Gantt chart, the main goals this week were to iron out our hardware and software design details and prepare the design presentation slides. We were able to accomplish the majority of this as a group, and some of us were even able to move ahead to initial implementation. One thing that I think we may need to make sure we do is to draft a parts list of parts we do not already have from inventory to order online as soon as possible.
Looking ahead, this upcoming week, Jay will be presenting our design review. Outside of class, I hope to have either an existing modified solution working or to start working on my own ML pipeline that can successfully classify the outputs that Jay has shared with me.