In this paper, we propose a novel method for highly efficient exploitation of unlabeled data-
Cooperative Learning. Our approach consists of combining Active Learning and Semi …

Adapting a writer-independent classifier toward the unique handwriting style of a particular
writer has the potential to significantly increase accuracy for personalized handwriting …

The automatic transcription of unconstrained continuous handwritten text requires well
trained recognition systems. The semi-supervised paradigm introduces the concept of not …

Data sparsity is one of the major bottlenecks in the field of Computational Paralinguistics.
Partially supervised learning approaches can help leverage this problem without the need of …

Handwritten text recognition is an active research area in pattern recognition. One of the
prerequisites of setting up a handwritten text recognizer is to train them using, mostly, large …

Deep learning approaches now provide state-of-the-art performance in many computer
vision tasks such as handwriting recognition. However, the huge number of parameters of …

We present a method for training an off-line handwriting recognition system in an
unsupervised manner. For an isolated word recognition task, we are able to bootstrap the …

Text recognition systems of today (aka OCRs) are mostly based on supervised learning of
deep neural networks. Performance of these is limited by the type of data that is used for …

Not all languages and domains of handwriting have large labeled datasets available for
training handwriting recognition (HWR) models. One way to address this problem is to …

Transportation has been one of the defining challenges of our age. Transportation decision
makers are facing difficult questions in making informed decisions. Activity-based travel …