Recognition of Handwritten Indic Script Using Clonal Selection Algorithm - Artificial Immune Systems - page 261

Fig. 1. Hundred random samples from the dataset of Bengali handwritten numerals

Performance of the proposed refinement stage is studied to check how rapidly the
system attains the maximum classification rate on the training set. In fact, it's the first
local maximum where the training terminates and at present, the system does not
attempt to find the global one. The response of the additional training module is
shown in fig. 2 for the dataset DS1. A similar behaviour is obtained for the other
dataset too.
In fig. 2 it is to be noted that the recognition accuracy gradually increases till the
8th iteration after which the accuracy degrades and training terminates. Number of
antigens undergo training in each pass is also plotted by a line curve in fig. 2. Please
note that iteration 0 represents the initial Phase-I training where all 10,000 antigens
were trained.

Table 1. Recognition accuracies and size of immune memory with two different training
algorithms

Recognition accuracy Size of immune memory

Dataset L1 L2 L1 L2

DS1 93.31% 96.23% 912 1283

DS2 92.57% 95.68% 1103 1472

Table 2. CPU Time for training and classification using two different training algorithms

Time to train Classification speed

(#characters per second)

Dataset L1 L2 L1 L2

DS1 5 H 14 Min 7 H 05 Min 52 49

DS2 5 H 19 Min 7 H 22 Min 51 47