An inclusive review on deep learning techniques and their scope in
  handwriting recognition
Sukhdeep Singh Sudhir Rohilla Anuj Sharma
Deep learning expresses a category of machine learning algorithms that have
the capability to combine raw inputs into intermediate features layers. These
deep learning algorithms have demonstrated great results in different fields.
Deep learning has particularly witnessed for a great achievement of human level
performance across a number of domains in computer vision and pattern
recognition. For the achievement of state-of-the-art performances in diverse
domains, the deep learning used different architectures and these architectures
used activation functions to perform various computations between hidden and
output layers of any architecture. This paper presents a survey on the existing
studies of deep learning in handwriting recognition field. Even though the
recent progress indicates that the deep learning methods has provided valuable
means for speeding up or proving accurate results in handwriting recognition,
but following from the extensive literature survey, the present study finds
that the deep learning has yet to revolutionize more and has to resolve many of
the most pressing challenges in this field, but promising advances have been
made on the prior state of the art. Additionally, an inadequate availability of
labelled data to train presents problems in this domain. Nevertheless, the
present handwriting recognition survey foresees deep learning enabling changes
at both bench and bedside with the potential to transform several domains as
image processing, speech recognition, computer vision, machine translation,
robotics and control, medical imaging, medical information processing,
bio-informatics, natural language processing, cyber security, and many others.