An inclusive review on deep learning techniques and their scope in handwriting recognition

TL;DR

This survey reviews deep learning techniques applied to handwriting recognition, highlighting recent progress, challenges like data scarcity, and the potential for transformative impacts across multiple domains.

Contribution

It provides a comprehensive overview of deep learning architectures in handwriting recognition and discusses future challenges and opportunities in the field.

Findings

Deep learning has achieved human-level performance in various recognition tasks.

Challenges include limited labeled data and the need for more revolutionary advances.

Promising progress suggests potential for broad applications beyond handwriting recognition.

Abstract

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.