Handwriting Recognition: Soft Computing and Probabilistic Approaches

Abstract

In this book, we introduce several methods for recognising unconstrained handwritten
words and digits using hidden Markov models (HMMs) and Markov random field
(MRF) models. Since the hidden Markov model (HMM) is stochastic finite state
automation, it is able to represent a sequence of features. we used HMMs to model
features that are extracted from outer contours of images to form sequences.
To overcome the limitation of HMMs in modelling structural information, we
used structural models, which are based on the best sequences of states, to
represent structural information and enhance the performance of HMMs. In
addition, we presented a procdure to model relationships between spectral
components using 2-D HMMs, where the spectral features are extracted by Fourier
descriptor. This method can be used to recognise two-dimensional shapes as well
as handwritten digits.

Markov random field models are appropriate to model two-dimensional features
of handwritten words and digits. The most important merit of Markov random
field models is that they provide flexible and natural methods for modelling
the interaction between spatially related random variables in their
neighbourhood systems via designed clique functions. In MRF model, the global
optimum can be derived from local information in term of clique functions.
This book also describes methods to use MRFs
to model structural relationships between line-segments for recognising
handwritten words and to model both structural and statistical information for
recognising handwritten digits. Relaxation labelling is used to maximise the
global compatibility of MRF models.

To evaluate the proposed methods, we had conducted experiments on two
databases: handwritten word database and handwritten digit database. Both
databases are taken from USPS CEDAR CDROM1. The recognition
rates for handwritten words are from 69.0% to 96.5% among top 1 to top 5
positions with only 7.5 training images per word on the average.
The recognition rates for handwritten digits range from 96.48\% to 98.37%
with different methods. These results show our method can achieve
recognition rates comparable to that reported in the literature recently.