Our researcher, Tonda Hoskovec, has long been thinking about the behavior of neural network training in the case of non-convex tasks with many local minima. This makes training difficult or inconsistent for many machine learning problems. Also the theory is lagging behind in practice and not much is guaranteed. A recent theoretical paper aims at solving this problem in a new ingenious way and caught Tonda’s attention. He decided to do the first experimental test of this theory. Is it practical, and does it work? Read on to learn the outcome!
Hear from Rossum’s Co-founder and Chief AI Architect Petr Baudis about our motivation behind the Rossum technology.
This is the second part of our special founders blog post on data capture technology and how Rossum represents a radically different approach to the whole problem of information extraction from business documents.
Last time we covered the fundamental issues of the traditional OCR systems due to their machine-like approach, in contrast with the magical efficiency of human mind in this task. Now, it’s time to delve into how exactly we replicated the human approach using deep learning, and show that it certainly is delivering as promised. Continue reading
Data capture for invoices ought to have been solved a long time ago! That’s what most people think, especially if they’ve never tried to actually do it.
That’s what we thought when we started talking to customers, looking for the ideal application of Rossum’s machine vision technology. It is genuinely surprising how hard this problem actually is, and how big an advantage a human mind has compared to a fixed algorithm. That’s also the reason Rossum’s approach stands out so much within this domain.
This is a special founder blogpost, in two parts written by the original minds behind Rossum’s technology – Petr, Tomas and Tomas. We will walk you through the concrete limitations of the current OCR systems, why we built Rossum, which lets anyone capture data from invoices without manual capture setup, and how it achieves this.
Who are we? Standard nerds, albeit with many big accomplishments between us in machine learning, computer vision, and AI. Just about 2 years ago, we decided to it was time to stop fiddling with AlphaGo and image recognition, and focus on one super-hard problem with a real impact on the lives of millions of people every day. Surprisingly enough, it turned out to be invoices. Here’s why: