This paper reviews the state of the art in off-line Roman cursive handwriting recognition. The
input provided to an off-line handwriting recognition system is an image of a digit, a word, or …

Neural network are most popular in the research community due to its generalization
abilities. Additionally, it has been successfully implemented in biometrics, features selection …

This chapter provides the background material and literature review for supervised
sequence labelling. Section 2.1 briefly reviews supervised learning in general. Section 2.2 …

Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the
best known results in unconstrained handwriting recognition. We show that their …

The success of deep learning based models have centered around recent architectures and
the availability of large scale annotated data. In this work, we explore these two factors …

Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of
segmenting cursive or overlap** characters, combined with the need to exploit …

In this paper we describe a database that consists of handwritten English sentences. It is
based on the Lancaster-Oslo/Bergen (LOB) corpus. This corpus is a collection of texts that …

For retrieving keywords from scanned handwritten documents, we present a word spotting
system that is based on character Hidden Markov Models. In an efficient lexicon-free …

The identification of a person on the basis of scanned images of handwriting is a useful
biometric modality with application in forensic and historic document analysis and …

This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural
Network (ANN) models for recognizing unconstrained offline handwritten texts. The …