5 Proposed Approac
The experiments described Various types of word im
represent the image into fea wrapper methods are perfo
which produce highest acc optimal significant features
individual’s writing.
5.1 Extracting Features
Feature extraction is a pro extracted features are in r
computed from digital imag shape, and provides a lot o
of the image [49]. Differen ‘where’ and others have be
of 4400 instances are extr divided into five different d
example of feature invarian each image.
T
Image Feature 1
0.217716 0.115774
0.369492 Extracted features can be di
and global features. Local relationships, meanwhile g
[50]. Good features are tho invariance and large inter-c
of authorship in WI.
Invarianceness of author writer is small compared
different words. This is due been proof in many researc
objective is to make con proposed techniques for s
authorship of invariancene significant feature which a
invarianceness of authorshi features of individual’s wri
invarianceness of authorsh es for Authorship Invarianceness in Writer Identification
ch
d in this paper are executed using the IAM database [4 mages from IAM database are extracted using UMI
ature vector. The selection of significant features using ormed prior the identification task. The selected featu
curacy from the identification task are identified as s for WI in this work and also known as unique feature
ocess of converting input object into feature vectors. T real value and unique for each word. A set of mome
ge using UMI represents global characteristics of an im f information about different types of geometrical featu
nt types of words from IAM database such as ‘the’, ‘an en extracted from one author. In this paper, a total num
racted to be used for the experiments, and are random datasets to form training and testing dataset. Table 1 is
nt of words using UMI with eight features vector for
able 1. Example of Feature Invariant
1 ………
Feature 7 Feature 8
6 ……….. 1.56976 1.82758
4 ……….. 0.0552545 0.499824 2 ………… 0.124305 0.580407
ivided into micro and macro feature classes which are lo features denote the constituent parts of objects and
global features describing properties of the whole obj ose satisfying two requirements which are small intra-cl
class invariance [51]. This can be defined as invariancen rship in WI shows the similarity error for intra-class sam
to inter-class different-writers for the same words e to the individual features of handwriting’s style which
chers such as [18], [24], and [52]. Related to this paper, ntributions towards this scientific validation using
selecting the significant features in order to proof ess in WI. The uniqueness of this work is to find
actually is the unique features of individual’s writing. T ip relates to individuality of handwriting with the uni
iting. The highest accuracy of selected features proofs hip for intra-class is lower than inter-class where e
48]. I to
the ures
the s of
The ents
mage ures
nd’, mber
mly the
the
ocal the
ject lass
ness me-
s or has
the the
the the
The ique
the each
individual’s writing contains the unique styles of handwriting that is different with other individual. To achieve this, the process of selecting significant features is
carried out using the proposed wrapper method before identification task.
5.2 Selecting Significant Features
