18-08-2014, 03:44 PM
An Ensemble Classifier For Offline Cursive Character Recognition
Using Multiple Feature Extraction Techniques
An Ensemble.pdf (Size: 1.02 MB / Downloads: 10)
INTRODUCTION
Cursive character recognition is one of the most important
areas in the field of pattern recognition. This importance is
due to many potential applications such as post mail sorting,
bank check analysis and so forth. The major problem in a
cursive character recognition system is the diversity of the
handwriting styles, which can be completely different for
different writers. This variability is so evident that some
systems can identify the writer based on a handwritten
text [1], [2], [3]. Even the same writer can change his writing
style due to variation on neurological conditions, type of the
pen and position [2]. These conditions make this one of the
most challenging problems in the computer vision field.
Another problem in this application is the high number
of classes to be considered, 52 different classes (26 lower
case and 26 upper case letters) which increases the number
of misclassifications between classes making this a more
complicated problem when compared to handwritten digit
recognition which has only 10 classes. For handwritten digit
recognition, recent works show a recognition rate superior to
99%. For character recognition the highest rates are around
90%. Therefore, breakthrough improvements can still be
done
CONCLUSIONS
In this paper, a method to increase cursive character
recognition rates by combining feature extractions methods
is proposed. Nine feature sets, two proposed by the authors
of this paper, are extracted and evaluated. The feature sets
were chosen using different approaches to add diversity to
the system.