Two things seem to be indisputable in the contemporary deep learning discourse: 1. The
categorical cross-entropy loss after softmax activation is the method of choice for …

Deep convolutional features for word images and textual embedding schemes have shown
great success in word spotting. In this work, we follow these motivations to propose an …

Imagine a world where people constantly try to pass through walls and cars suddenly leave
the roads and drive into rivers. Although this is unrealistic, most simulations for mobile ad …

Abstract Handwritten Word Recognition and Spotting is a challenging field dealing with
handwritten text possessing irregular and complex shapes. The design of deep neural …

We present a framework for learning an efficient holistic representation for handwritten word
images. The proposed method uses a deep convolutional neural network with traditional …

Accurate wind speed forecasting can improve energy management efficiency and promote
the use of renewable energy. However, the inherent nonlinearity and fluctuation of wind …

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 …

Current tasks and methods in Document Understanding aims to process documents as
single elements. However, documents are usually organized in collections (historical …

This thesis, first defines the goal of Keyword Spotting from a Decision Theory perspective.
Then, the problem is tackled following a probabilistic formulation. More precisely, Keyword …