Multiscale analysis for off-line handwriting recognition

The aim of this thesis is to investigate how 'multiscale analysis' can help to solve some of the problems associated with achieving reliable automatic off-line handwriting recognition based on feature extraction and model matching. The thesis concentrates on recognising off-line handwriting, in which no explicit dynamic information about the act of writing is present. Image curvature has emerged as being an important feature for describing and recognising shapes. However, it is highly susceptible to noise, requiring smoothing of the data. In many systems, smoothing is performed at a pre-determined fixed scale. The feature of this work is that Multiscale analysis is performed by applying Gaussian smoothing over a 'range' of octave separated scales. This process not only eliminates noise and unwanted detail, but also highlights and quantifies those features stable over a 'range' of scales. Curvature features are extracted by evaluating the 1[sup]st and 2[sup]nd order derivative values for the Gaussian kernels, and a method is proposed for automatically selecting those scales of significance at which to perform optimum matching. A set of describing elements (features) is defined, and combined into a representation known as "codons" for matching. Handwritten characters are recognised in terms of their constituent codons, following the process of multiscale analysis. This is done by extracting codons from a range of octave separated scales, and matching the codons at scales of significance with a database of model codons created for the different types of handwritten characters. Other approaches for matching are reviewed and contrasted, including the use of artificial neural networks. The main contribution of this thesis is the investigation into applying multiscale analysis to ascertain the most appropriate scale(s) at which to perform matching by removing noise, ascertaining, and extracting features that are significant over a range of scales. Importantly, this is performed without having to pre-determine the amount of smoothing required, and therefore avoiding arbitrary thresholds for the amount of smoothing performed. The proposed method shows great potential as a robust approach for recognising handwriting.