Classification of cervical cells in Pap smear images is a challenging task due to the
limitations these images exhibit and the complexity of the morphological changes in the …

Handwritten text recognition has been developed rapidly in the recent years, following the
rise of deep learning and its applications. Though deep learning methods provide notable …

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 …

Deep convolutional neural networks are today the new baseline for a wide range of machine
vision tasks. The problem of keyword spotting is no exception to this rule. Many successful …

Image deformations under simple geometric restrictions are crucial for Handwriting Text
Recognition (HTR), since different writing styles can be viewed as simple geometrical …

Keyword spotting (KWS) is defined as the problem of detecting all instances of a given word,
provided by the user either as a query word image (Query-by-Example, QbE) or a query …

The established paradigm in handwriting recognition techniques involves supervised
learning, where training is performed over fully labelled (transcribed) data. In this paper, we …

Benchmark datasets are important in document image processing field, as they allow to
analyze different approaches and compare their performances in a fair manner. There exist …

Urdu word spotting is among the most challenging tasks in image processing and word
spotting of hand written Urdu text is even more so. When it comes to handwritten Urdu …

In this work, we present a novel approach for the extraction of deep features from a
Convolutional Neural Network (CNN), designed for the task of Keyword Spotting (KWS). The …