Vast collections of documents available in image format need to be indexed for information
retrieval purposes. In this framework, word spotting is an alternative solution to optical …

Abstract We propose Deep Feature Interpolation (DFI), a new data-driven baseline for
automatic high-resolution image transformation. As the name suggests, DFI relies only on …

In recent years, deep convolutional neural networks have achieved state of the art
performance in various computer vision tasks such as classification, detection or …

The recent past has seen CNNs take over the field of word spotting. The dominance of these
neural networks is fueled by learning to predict a word string embedding for a given input …

From the early days of pattern recognition, word spotting have been important test beds for
studying how well machines can perform better decision making. In recent years, word …

Word spotting has become a field of strong research interest in document image analysis
over the last years. Recently, AttributeSVMs were proposed which predict a binary attribute …

The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up
an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining …

Convolutional Neural Networks have made their mark in various fields of computer vision in
recent years. They have achieved state-of-the-art performance in the field of document …

This paper presents an efficient and robust approach for reducing the size of deep neural
networks by pruning entire neurons. It exploits maxout units for combining neurons into more …

Deep features, defined as the activations of hidden layers of a neural network, have given
promising results applied to various vision tasks. In this paper, we explore the usefulness …