The recognition of handwritten digits is an application which
has been used as a benchmark for comparing shape recognition methods.
We train COSFIRE filters to be selective for different parts of handwritten
digits. In analogy with the neurophysiological concept of population
coding we use the responses of multiple COSFIRE filters as a shape descriptor
of a handwritten digit. We demonstrate the effectiveness of the
proposed approach on two data sets of handwritten digits: Western Arabic
(MNIST) and Farsi for which we achieve high recognition rates of
99.52% and 99.33%, respectively. COSFIRE filters are conceptually simple,
easy to implement and they are versatile trainable feature detectors.
The shape descriptor that we propose is highly effective to the automatic
recognition of handwritten digits.peer-reviewe
