Content based image retrieval is a field in computer vision. The aim is to find the most similar images to a given input image, where the similarity refers to the sem
We had the unique opportunity to develop an image captioning system combining computer vision and NLP from a prototype model to a fully scalable data product with a team of five interdisciplinary students from the TUM Data Innovation Lab during a period of six months as part of an educational research experience.
tl;dr Data Science, Machine Learning Engineering, Software Engineering, and IT-Operations know-how is required to turn a prototypical machine-learning model into an end-
Machine Learning on the Edge becomes more and more important for Smart Cities. We investigate how Deep Learning models can be optimized and deployed on edge devices for smart parking guidance systems.
This blog post investigates how deep learning models can be optimized and deployed on edge devices for parking guidance systems. I will present two different approach
In this article I describe the steps and approaches to image recognition for receipt digitalization using computer vision. This is the basic functionality behind apps such as Google Lens, Evernote, PaperScan and taggun.io.
“Would you like the receipt?”—It’s hard to say no to that. Not because you actually want it (you may even throw it in the trash before exiting the store), but because
We built a text spotting (OCR) pipeline that out-performed Google Cloud Vision using semi-supervised Generative Adversarial Networks.
Despite all advances in machine learning due to the advent of deep learning, the latter has one major shortcoming: It requires a lot of data during the learning proce