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Compared to these approaches and achievements, the proposed AIS-based method
can be viewed as a potential alternative. However, it is to be noted that no study em-
ploys the same feature set. Authors in [17] use some grid-based features, [18] consid-
ers wavelet coefficients as features whereas, a size normalized binary image array has
been used as feature in the present study. Use of distance measure also differs from
one study to another. Therefore, a direct comparison needs replication of these ex-
periments using a uniform feature set and the same distance measure. Our future
study will consider this aspect to bring out a judicious comparison between an AIS-
based framework and other approaches using different learning paradigm.
4 Conclusions
This paper presents an application of a clonal selection algorithm for recognition of
handwritten Indic numerals. In particular, a 2-phase clonal selection algorithm im-
plementing a retraining scheme is proposed, and experiments using different datasets
are performed. Reported results show that this new method outperforms the previ-
ously used single pass method. Overall classification performance shows that this
method compares well with the existing approach. In particular, the proposed scheme
achieves recognition accuracy of about 96% that is comparable to the previous ap-
proaches.
This study uses a feature vector and a simple distance measure to explore the feasi-
bility of an AIS-based approach as an alternative classification tool. Since encourag-
ing results have been obtained in this experiment, future extension of this study would
include examination of different feature sets and distance measures to further improve
the recognition accuracy.
Reference
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