Neural Networks for Handwriting Recognition - Computational Intelligence Paradigms in Advanced Pattern Classification - page 5

Chapter 2
Neural Networks for Handwriting Recognition
Marcus Liwicki 1 , Alex Graves 2 , and Horst Bunke 3
1 German Research Center for Artificial Intelligence, Trippstadter Str.
122, 67663 Kaiserslautern, Germany
e-mail: [e-mail]
2 Institute for Informatics 6, Technical University of Munich, Boltzmannstr.
3, 85748 Garching bei München, Germany
e-mail: [e-mail]
3 Institute for Computer Science and Applied Mathematics,
Neubrückstr. 10, 3012 Bern, Switzerland
e-mail: [e-mail]
Abstract. In this chapter a novel kind of Recurrent Neural Networks (RNNs) is
described. Bi- and Multidimensional RNNs combined with Connectionist Tem-
poral Classification allow for a direct recognition of raw stroke data or raw pixel
data. In general, recognizing lines of unconstrained handwritten text is a challeng-
ing task. The difficulty of segmenting cursive or overlapping characters, combined
with the need to assimilate context information, has led to low recognition rates
for even the best current recognizers. Most recent progress in the field has been
made either through improved preprocessing, or through advances in language
modeling. Relatively little work has been done on the basic recognition algo-
rithms. Indeed, most systems rely on the same hidden Markov models that have
been used for decades in speech and handwriting recognition, despite their well-
known shortcomings. This chapter describes an alternative approach based on a
novel type of recurrent neural network, specifically designed for sequence labeling
tasks where the data is hard to segment and contains long-range, bidirectional or
multidirectional interdependencies. In experiments on two unconstrained
handwriting databases, the new approach achieves word recognition accuracies of
79,7% on on-line data and 74,1% on off-line data, significantly outperforming a
state-of-the-art HMM-based system. Promising experimental results on various
other datasets from different countries are also presented. A toolkit implementing
the networks is freely available for public.
1 Introduction
Handwriting recognition is traditionally divided into on-line and off-line recogni-
tion. In on-line recognition a time ordered sequence of coordinates, representing