In this stage, the features of the characters that are crucial for classifying them at recognition
stage are extracted. This is an important stage as its effective functioning improves the
recognition rate and reduces the misclassification [17]. Diagonal feature extraction scheme for
recognizing off-line handwritten characters is proposed in this work. Every character image of
size 90x 60 pixels is divided into 54 equal zones, each of size 10x10 pixels (Fig.3(c)). The
features are extracted from each zone pixels by moving along the diagonals of its respective
10X10 pixels. Each zone has19 diagonal lines and the foreground pixels present long each
diagonal line is summed to get a single sub-feature, thus 19 sub-features are obtained from the
each zone. These 19 sub-features values are averaged to form a single feature value and placed
in the corresponding zone (Fig.3 (b)). This procedure is sequentially repeated for the all the
zones. There could be some zones whose diagonals are empty of foreground pixels. The feature
values corresponding to these zones are zero. Finally, 54 features are extracted for each
character. In addition, 9 and 6 features are obtained by averaging the values placed in zones
rowwise and columnwise, respectively. As result, every character is represented by 69, that is,
54 +15 features.