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 …

Along with the explosive growth of the amount of handwritten documents that are preserved,
processed and accessed in a digital form, handwritten document images word spotting has …

In this paper we present an efficient segmentation-free word spotting method, applied in the
context of historical document collections, that follows the query-by-example paradigm. We …

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 …

Cuneiform tablets appertain to the oldest textual artifacts used for more than three millennia
and are comparable in amount and relevance to texts written in Latin or ancient Greek …

In this paper we propose an unsupervised segmentation-free method for word spotting in
document images. Documents are represented with a grid of HOG descriptors, and a sliding …

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 …

The classification of acoustic events in indoor environments is an important task for many
practical applications in smart environments. In this paper a novel approach for classifying …

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 …

Word spotting allows to explore document images without requiring a full transcription. In the
query-by-string scenario considered in this paper, it is possible to search arbitrary keywords …