Latest posts

FairScan handles all image processing steps automatically. With FairScan 1.7, I could clearly see that the app was not doing a great job at distinguishing color from grayscale documents. Some users’ comments confirmed it. I needed to improve that. However, if I change anything in this processing, how can I know whether what I do is actually better, or worse, than before?

My ambition with FairScan is not just to build an open-source app: it's to make an app that everyone can use, including all the people who don't know what GitHub is. That's definitely not easy and it takes more than writing good code. Before starting FairScan, I already knew there was a big difference between good software and a good product. Now, I experience it very concretely. This is my daily reality.

To automatically detect documents, FairScan uses an image segmentation model. The dataset needs to reflect the situations the model should be able to handle, so the model's accuracy depends heavily on the quality and size of that data. For FairScan, I decided to build a dedicated dataset and to make it public. That turned out to be quite an experience.

FairScan aims to be very simple, so that one can get a PDF in just a few seconds. To achieve that, the app relies heavily on automatic processing... but that's easier said than done. The very first review I got on the Play Store read something like: "It doesn't work, 'no document detected' on every page". Automatic document detection isn't just one feature among others: it's the feature that can make or break the whole experience. I had to do something.

I often describe FairScan as simple and respectful. But what does "respectful" really mean when we talk about apps? We live in a world where there’s an app for everything, and we’re constantly pushed to install more. Yet almost no one talks about "respectful apps". A quick search online gives barely a few hundred results. In other words: it’s not even a topic. So let me explain what I mean when I use that word.

How does FairScan work at its core? How does it transform an image captured by the phone's camera into the page of a PDF document that doesn’t just look like a photo? From the user’s point of view, it feels like a single transformation that happens in less than a second. Let’s look at the different steps involved, illustrated with a visual example.

This first post is about the vision behind FairScan: what I'm trying to achieve, and what I'm not aiming for. FairScan is my attempt to build something different: simple (getting a PDF in seconds, without effort) and respectful (no ads, no account, no tricks). It doesn't try to do everything, but it focuses on doing its job well.