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Recognition of Handwritten Indic Script Using Clonal
Selection Algorithm
Utpal Garain
1
, Mangal P. Chakraborty
1
, and Dipankar Dasgupta
2
1
Indian Statistical Institute, 203, B.T. Road, Kolkata 700108, India
2
The University of Memphis, Memphis, TN 38152
utpal@isical.ac.in, dasgupta@memphis.edu
Abstract.
The work explores the potentiality of a clonal selection algorithm in
pattern recognition (PR). In particular, a retraining scheme for the clonal selec-
tion algorithm is formulated for better recognition of handwritten numerals (a
10-class classification problem). Empirical study with two datasets (each of
which contains about 12,000 handwritten samples for 10 numerals) shows that
the proposed approach exhibits very good generalization ability. Experimental
results reported the average recognition accuracy of about 96%. The effect of
control parameters on the performance of the algorithm is analyzed and the
scope for further improvement in recognition accuracy is discussed.
Keywords:
Clonal selection algorithm, character recognition, Indic scripts,
handwritten digits.
1 Introduction
Several immunological metaphors are now being used (in a piecemeal) for designing
Artificial Immune Systems (AIS) [1]. These approaches can broadly classified into
three groups namely, immune network models [2], negative selection algorithms [3],
and clonal selection algorithms [4]. This paper investigates a new training approach
for clonal selection algorithm (CSA) and its application to character recognition.
Earlier CSA was used for a 2-class problem to discriminate pair of similar character
patterns [5], the present study extends it for a m-class classification problem.
Training in CSA so far is modeled as one pass method where each antigen under-
goes single training phase. Once the training on all antigens is over, an immune mem-
ory is produced and used for solving classification problem (as used in [5] and [6]).
Our work presents a new training algorithm where a refinement phase is used to fine-
tune the initial immune memory that is build from the single pass training. In the
refinement stage, training of an antigen depends on its recognition score. Incorrect
recognition of an antigen triggers further training. This process continues until the
immune system suffers from negative learning or it is over-learned.
Recognition of handwritten Indic numerals has been considered to study the perform-
ance of the modified CSA. Because of its numerous applications for postal automation,
bank check reading, etc., the document image analysis researchers have been studying
the problem for last several years and a number of methods have been proposed.
