In research on handwriting style since it could be

In spite of current technological advances, there are
not still algorithms allowing a computer to transcript the content of any
“difficult” handwritten document (e.g. a historical document). The general
handwriting recognition problem presents many difficulties produced by
interpersonal and intrapersonal variations when writing, the cursive nature of
handwriting, the use of different pen types or the presence of paper with noisy
background. It has been studied and determined with scientific rigor the
individuality of handwriting. Regarding the handwriting recognition problem,
there are two variants: offline and online recognition. The
offline problem consists in recognizing handwritten text that has previously
been written on paper, and then digitized. The online handwriting problem aims
to recognize the text that was written using some kind of electronic device.
The sensors of this device also record a set of dynamic measures about how the
act of writing is produced (e.g. writing pressure, pen altitude and azimuth,
among others). In recent years, there has been more progress on the online
modality but the offline one is still far to be solved in an unrestricted
manner.

Psychology can also get benefits from research on
handwriting style since it could be possible to identify correlations between
the handwriting and some personality attributes of the writer. In the field of
Human-Computer Interaction, if gender of a user can be automatically predicted,
the computer applications could offer him/her a more personalized interaction
(e.g. gender-oriented advertising). Biometric Security can also benefit from
handwriting prediction since this fact can be combined with other biometric
modalities in order to improve security when accessing computer systems.

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These handwriting-based demographic prediction
problems include gender, handedness, age ranges or even nationality of a person.
This group of supervised learning problems can be considered as binary or
multi-class ones. The most common binary problems are gender prediction (where
handwriting texts can be classified as written by men or by women), and
handedness prediction (where handwriting texts can be classified as produced by
right-handed or by left-handed writers). Among the multi-class problems, one
can discriminate among texts written by people included in different age
intervals, in specific human races or even in groups of nationalities. A
property of all these problems is that they can be either balanced (i.e. where
approximately half of the population belong to each class) as in the case of
gender classification, or they can be unbalanced as it is the case of the
handedness classification. In general, these demographic classification
problems are very complex, even for humans, since it is quite difficult to find
which handwriting features properly characterize each involved class. An
example of this occurs in the classification of gender. Although it is accepted
that feminine writing is rounder and neater than masculine one, there are some
cases where masculine writing may have a “feminine” appearance and
vice versa. In this paper, we additionally aim to analyze the relationships
between the gender handwriting features.

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