Document image classification is not as well studied as natural image classification. We experimented with different neural network architectures on document image dataset. We discuss our preliminary results in this post.
Deep Image Prior defies the idea that “deep learning only works in the context of massive datasets or models pretrained on such datasets”. This paper showed that some deep neural networks could be successfully trained on a single image without large datasets, the structure of the network itself could be preventing deep networks from overfitting.
This post presents a walk through of an object detection process applied to Audio/Video receiver back panel images. 1
Screening through hundreds of mail and prioritizing your work for the day is a difficult job. One kind of prioritization that is important is dealing with deadlines that are indicated in emails such as a slide deck or report due. The first step of calendarizing deadlines is to identify that a deadline is indeed present. We’ve tried to tackle this deadline tagging as an email classification problem using a Bayesian approach with the result being PMail. We also built an RNN based model which we compare with the Bayesian approach. We were able to show promising results with the approaches we took, which we detail in this post.
Approximate biometrics are often required for effective online shopping experience, for example, for clothing, eyewear and footwear. We experimented with Mask-RCNN based object segmentation for measuring human feet with the intention of recommending appropriate footwear, which we talk about in this post.
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!
In this post, we talk about a filter we recently developed and deployed 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.