Style classification of cursive script recognition

Dehkordi, ME, 2003. Style classification of cursive script recognition. PhD, Nottingham Trent University.

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Abstract

Handwriting recognition has been the subject of intensive research for many years. However, despite the best effort of many researchers, the problem of handwriting recognition is far from being solved. The greatest difficulty iii cursive script recognition is due to the large variation of shapes that can result from the different writing styles. A common way to address this problem is to accommodate the variability in the feature set. However, such systems are limited in the range of writing styles that they can successfully deal with. An alternative approach has been to minimise the variability within the handwriting itself. Techniques such as normalisation, slant correction, restricting the number of objects to be recognised (i.e. numerical character, name of city) etc. have been shown to be partially effective. However further work remains to be done in order to cope with the variation problem. Here it is hypothesised that a pre-classification of writer style would provide an effective means of managing style variation and hence achieve better recognition results.

The main aim of this thesis is to investigate alternative ways of addressing problems brought about by the variability of human handwriting; in particular those problems related to the recognition of off-line cursive handwriting. Style has been further broken down into case and quality of handwriting. Case classification of handwriting is proposed as a means of limiting die size of the template database used for word recognition. The quality of handwriting has been defined in terms of its legibility. It is proposed that this approach would lead to determining the legibility of an unknown sample prior to recognition. So as to select a recogniser that is suited to the quality of handwriting of the unknown sample.

Two non-parametric classification techniques are applied to features extracted from the word image contours in order to compare their effectiveness in classifying words into upper, lower and mixed cases and further into legible, illegible and middle (between legible and illegible) classes. In the first method, a Multiple Discriminant Analysis (MDA) is used to transform the space of the extracted feature (36 dimensions) into an optimal discriminate space for a nearest mean based classifier. In the second method, a Probabilistic Neural Network (PNN) based on Bayes strategy and non-parametric estimation of probability density function is used. The experimental results show that PNN gives superior classification results when compared to MDA for both types of style classification.

A number of experiments have been carried out using unseen data to determine the effectiveness of the above techniques. For a two-class word case classification problem the PNN approach yields 100% (lower/upper), 88%(upper/mixed) and 81%(lower/mixed) correct classification. For three-class word case classification the rate of correct classification is 73%. The same approach when applied to legible, illegible and middle style classification handwriting provides 86.5% (legible/illegible), 75.5% (legible/middle) and 90.5% (middle/illegible) correct classification for two classes. For three-class legibility classification the rate of correct classification is 67.33%.

Style variation remains an open subject for further research. Word case and legibility are demonstrated to provide positive steps towards a more tangible definition of style. This research has demonstrated that a holistic classification technique is effective in dealing with the concept of style in a quantifiable manner. The experimental results indicate that further word level features are needed to further improve classification. This together with additional style categories would lead to more effective means of managing variability.

Item Type:	Thesis
Creators:	Dehkordi, M.E.
Date:	2003
ISBN:	9781369314854
Identifiers:	Number Type PQ10183219 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	30 Sep 2020 14:42
Last Modified:	03 Aug 2023 11:15
URI:	https://irep.ntu.ac.uk/id/eprint/41038

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