arrow_back Deep Type - deep convolutional neural networks for style transfer in typography
Typography detection using Deep Convolutional Neural Networks
Submitted by Pallavi Ramicetty (@pallaviramicetty) on Saturday, 29 April 2017
Section: Crisp talk Technical level: Intermediate
Keeping undesirable content out of social networks and communication channels is a common problem. Our email systems today have sophisticated “spam filters” thanks to which we’re protected from much harm and waste of time. The problem of spam is particularly harsh in niche social networks and interest groups which are small and sensitive to disruption. We run one such niche social network for typography enthusiasts called Fontli and we like to protect our dear typographers from content that they’re not interested in - which is everything that isn’t typography. The problem is that this is hard … even for humans!
We recently developed and deployed a filter to reduce and flag incidences of non-typographic content on Fontli, using a deep convolutional neural network based image classifier. We’ve had modest success and faced some intriguing situations and results along the way.
Discussion on the challenges faced in typography detection of the Fontli app.
Basic knowledge on Machine Leaning and Deep Learning
Done with my Masters degree (M.Tech) in Jawaharlal Nehru Technological University, Anantapur. Currently, working with Imaginea Technologies Inc. Passionate about Machine Learning and Deep Learning.