Grant: Discovery Grants Program - Individual
Concordia University
$48,000.00
Computational Analysis of Handwriting, Character Recognition, and Design of Digital Fonts
FY 2020-21
Summary
Over the past decades, we have explored and developed numerous computational
techniques of recognizing different types of patterns, including but not
limited to handwriting in different languages, and human faces of multiple
age groups. This Discovery grant application is related to novel techniques of
discovering new knowledge-based
features, methods of reducing the error rate
and optimizing the combination of different recognition algorithms, and
the recognition of characters and words written in English, French, Arabic, and
other languages. It is aimed at improving our recognition algorithms
using multi-stage multiple classifiers by incorporating the new features
discovered in our tachistoscope and eye-tracking experiments during the
past years. It will be
focused on an extension of my research in the
following
new areas in
consultation with experts in graphology and psychology,
and font designers:
- Extract more detailed distinctive handwriting features, such as the
shapes of characters, the way characters are formed, and peculiarities
of the writing of the individuals, to validate the
correlation between
handwriting and personality characteristics, which is
a challenging and
controversial topic in graphology,
and to detect the health conditions of
the writers, e.g. those with Parkinson and Alzheimer diseases. We believe
our rich experience in handwriting recognition will enable us to bring new
insights to this subject and offer help to graphologists in saving their
time to analyze the handwriting of individuals.
- Apply our character recognition and perceptive techniques to assess
the legibility of the fonts used in the license plates of automobiles and
propose new designs of fonts to facilitate the recognition of the license
plates by image processing and pattern recognition techniques to help
law-enforcement organizations in finding stolen cars, traffic offences,
identification vehicles under different weather conditions and the command
of driverless vehicles.
- Extend our feature extraction techniques, such as the extraction of keypoints
of the characters minted on the coins or printed on banknotes, to detect
fake coins and paper
money, which has become an imminent subject of
importance affecting the
economy of all countries around the whole world.
- To conduct intensive experiments on very large sets of data to discover new
ways of helping the social and economic well-being of our society.
Program
Discovery Grants Program - Individual
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2019-07005
Name nom
Suen, Ching
Department département
Computer Science and Software Engineering
Organization id
70
Institution établissement
Concordia University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2019
Award amount
48000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2700
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information technology
Research subject fr
Technologie de l'information
Installment
inst:2-5
Partie
4
Nb partie
5
Application title
Computational Analysis of Handwriting, Character Recognition, and Design of Digital Fonts
Keyword
NSERC
Application summary
Over the past decades, we have explored and developed numerous computational
techniques of recognizing different types of patterns, including but not
limited to handwriting in different languages, and human faces of multiple
age groups. This Discovery grant application is related to novel techniques of
discovering new knowledge-based
features, methods of reducing the error rate
and optimizing the combination of different recognition algorithms, and
the recognition of characters and words written in English, French, Arabic, and
other languages. It is aimed at improving our recognition algorithms
using multi-stage multiple classifiers by incorporating the new features
discovered in our tachistoscope and eye-tracking experiments during the
past years. It will be
focused on an extension of my research in the
following
new areas in
consultation with experts in graphology and psychology,
and font designers:
- Extract more detailed distinctive handwriting features, such as the
shapes of characters, the way characters are formed, and peculiarities
of the writing of the individuals, to validate the
correlation between
handwriting and personality characteristics, which is
a challenging and
controversial topic in graphology,
and to detect the health conditions of
the writers, e.g. those with Parkinson and Alzheimer diseases. We believe
our rich experience in handwriting recognition will enable us to bring new
insights to this subject and offer help to graphologists in saving their
time to analyze the handwriting of individuals.
- Apply our character recognition and perceptive techniques to assess
the legibility of the fonts used in the license plates of automobiles and
propose new designs of fonts to facilitate the recognition of the license
plates by image processing and pattern recognition techniques to help
law-enforcement organizations in finding stolen cars, traffic offences,
identification vehicles under different weather conditions and the command
of driverless vehicles.
- Extend our feature extraction techniques, such as the extraction of keypoints
of the characters minted on the coins or printed on banknotes, to detect
fake coins and paper
money, which has become an imminent subject of
importance affecting the
economy of all countries around the whole world.
- To conduct intensive experiments on very large sets of data to discover new
ways of helping the social and economic well-being of our society.