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We propose a novel perception driven feature extraction called binary external symmetry axis constellation (BESAC) and a fast Boolean matching character recognition technique. A constellation model using a set of external symmetry axes which are perceptually significant can uniquely represent a handwritten character pattern. This model generates two histograms of orientations that are binary coded...
In this paper, a new sparse concept coding based image representation (SCCST) is proposed which efficiently extracts low dimensional features from the spatio-spectral decomposition of handwritten characters. The multiresolution decomposition is obtained by adopting an octave sampling based non-redundant S-transform. The introduction of sparsity not only reduces feature dimension significantly, but...
Recognition of handwritten scripts has always been a challenging task before the character recognition community. The difficulty lies in the fact that different individuals have different writing styles and hence there is a lot of intra-class pattern variation. Several feature extraction techniques based on statistical, structural properties have been reported in literature. We, in this paper, propose...
Feature extraction is an important stage which decides the accuracy of any character recognition system. The state-of-the-art feature extraction can be categorized to be either spatial domain based, transform domain based or a hybrid combination of both. We propose a new feature extraction method based on the non-redundant Stockwell Transform (ST), which takes care of the redundancy as well as computational...
Unconstrained handwritten character recognition is a major research area where there is a lot of scope for improving accuracy. There are many statistical, structural feature extraction techniques being proposed for different languages. Many classifier models are combined with these features to obtain high recognition rates. There still exists a gap between the recognition accuracy of printed characters...
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