Publication

Transforming the feature vectors to improve HMM based cursive word recognition systems

2002
Reports

Abstract

Although many Offline Cursive Word Recognition systems are based on HMMs, no attention was ever paid, to our knowledge, to the fact that the feature vectors are typically not in the most suitable form for modeling. They are most of the time correlated and embedded in a space of dimension higher than their Intrinsic Dimension. This leads to several problems and has a negative influence on the performance. By applying some transforms it is possible to solve, or at least to attenuate, such problems resulting in data easier to model and in systems with higher recognition rate. In this work, we used Principal Component Analysis (linear and nonlinear) and Independent Component Analysis. A reduction of the error rate by up to 30.3% (over single writer data) and 16.2% (over multiple writer samples) is shown to be achieved.

Official source

https://infoscience.epfl.ch/entities/publication/b6d0455a-684f-4a8f-aedf-3c1d380559fc

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2002
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/b6d0455a-684f-4a8f-aedf-3c1d380559fc

About this result

Related concepts (32)

Principal component analysis

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.

Kernel principal component analysis

In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space. Recall that conventional PCA operates on zero-centered data; that is, where is one of the multivariate observations.

Dimension

In physics and mathematics, the dimension of a mathematical space (or object) is informally defined as the minimum number of coordinates needed to specify any point within it. Thus, a line has a dimension of one (1D) because only one coordinate is needed to specify a point on it - for example, the point at 5 on a number line. A surface, such as the boundary of a cylinder or sphere, has a dimension of two (2D) because two coordinates are needed to specify a point on it - for example, both a latitude and longitude are required to locate a point on the surface of a sphere.

Related publications (32)

Transforming the feature vectors to improve HMM based cursive word recognition systems

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