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People are still using handwritten documents in their daily life (Business meetings, Lectures, To-do lists, etc.). But these are easy to misplaces and the content is not that accessible and understandable. The contemporary answer is digitalization of this content.
Handwriting are proven to help people memorize the content better (1). Thus, even though apps exists, which try to answer the needs and requirements in this field, we believe that there is an area besides digitalization, that can be explored. We see this area as optimization of searchability and content-aware analyzation possibilities.
(1) Comparing Memory for Handwriting versus Typing, J. Smoker et al.
What it does
Textoru is an AI-webapp that optimizes handwritten documents, by allowing the user to digitize previously scribbled notes, and collect them in a database. This way, the user can research within their newly sorted collection of notes, and receive advanced search results from our AI.
The advanced search features are able to refer to both direct mentions of searched words and indirect references. For instance, being able to understand that when “my sister loves her dog, he is very caring”, the “he” refers to the earlier mentioned dog. This is called co-referencing. The content is also optimized by doing entity recognition of subjects in the text (people, companies, places, etc.), to provide more insight of the text, giving a better overview to the user.
These optimizations aids the user in being more efficient, effective, and engaged.
How we built it, and the challenges we faced along the way
In order to create this artificial intelligence, we`ve been through a design-process that has involved several exercises to help shape and built our platform.
We spent the entire friday evening trying to enhance our creativity by doing exercises that boost that aspect of the game. Once we felt confident that our minds were opened up enough, we started brainstorming and used our time coming up with project ideas for the different tracks and challenges. By the end of friday, we had at least 50 post-its and a bit of high level software architecture, to give an idea of subjects and areas that we found interesting enough to engage ourselves in.
We had a big meeting saturday noon, and figured that we’d go for the AI track, and try to make an analog text to digital converter, using a phone to take pictures of handwritten notes/documents. We started developing this platform on several different fronts; 2 were dedicated to make the AI algorithms behind the database storing all of this information, one were developing an android app that would hold the algorithm and send the pictures to our server. And some were also designing the interactions/UI of the app, and one would make a front-end website, that would send POST, and GET HTTP requests..
However, this all proved a major challenge, that would eventually lead us to stop developing the app to take and send the pictures for the server, since it was way too much work for just one person to do alone. Also the AI algorithms did a big number on us, having half of our group dedicating almost all of their time to make this part of our project work. Later on the day we would then send 2 people to go and research and interview some people in the hackathon, to get a better understanding, of when and where people e.g. students use notebooks, and write documents in the hand. We then made a persona and a use case scenario where we focused our energy on realising as much as possible from that vision. Saturday evening we designed the user experience of the “ideal app” (that would be the product we would have made if we had the time), and visualized the interaction with the app.
Sunday noon we finally had the front-end sync up with the database/server and AI we had built the day before, which was the entire problem that we were worried we wouldn’t get to finish in time. But it has been optimized and finished to an extend, where it is representing our platform in a way we deem fit.
Accomplishments that we're proud of
We believe that our product could be commercialized and that the features we defined bring a “plus” to the existing market of digitalization. We are happy to have worked on a proof of concept and case study, which let’s know how digitalization can afford improvement and optimization for the benefit of the user. In the short time and the given conditions, we are proud of having an AI and a search engine working and being able to show it.
What we learned
How to collaborate with people with various background and experiences and improved our own individual skills. We also trained our rapid product development skills and mental resolve in stressful conditions.
What's next for Textoru
We have a lot of new further ideas which could improve the user experience et make the product even more commercially appealing. Some part of the team may want to develop it further.