Yongha Hwang

Looking back, these past two weeks have been denser and more meaningful than all the time I spent building space ocr alone.

I asked six developers to build a demo with the space ocr API. What they brought back came straight out of the problems they deal with in their own work use cases I hadn't imagined. Getting to see that, and getting to talk with them about it, was probably the most valuable stretch of time in the whole life of this project.

In my first update post I turned down a suggestion. Someone with nineteen years in banking software asked for control totals, checking that the numbers on a document add up, and I said no. Getting there meant giving fields types, then converting formats, then summing, then comparing, and I thought all four of those belonged to a layer above this one. I have now built the first two, and I want to explain what moved.

What moved was not the argument. It was hearing the same thing from people who had never read that thread.

Last week I ran space ocr against Mistral's Document AI, and the number that stayed with me afterwards was not the one in the headline.

The benchmark. Same document photos, same fields, same scorer, three runs each. Across 463 fields, space ocr came out at 91.1% field accuracy and Mistral at 70.0%, and the repeated runs were considerably more stable on my side. Full write-up with the individual cases and where the differences actually showed up: https://space-ocr.com/articles/s... and https://medium.com/@oisidonut/co...