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Handwriting Analysis for Disease Identification

Syeda Asra1 , Shubhangi D.C2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 251-254, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.251254

Online published on Sep 30, 2018

Copyright © Syeda Asra, Shubhangi D.C . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Syeda Asra, Shubhangi D.C, “Handwriting Analysis for Disease Identification,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.251-254, 2018.

MLA Style Citation: Syeda Asra, Shubhangi D.C "Handwriting Analysis for Disease Identification." International Journal of Computer Sciences and Engineering 6.9 (2018): 251-254.

APA Style Citation: Syeda Asra, Shubhangi D.C, (2018). Handwriting Analysis for Disease Identification. International Journal of Computer Sciences and Engineering, 6(9), 251-254.

BibTex Style Citation:
@article{Asra_2018,
author = {Syeda Asra, Shubhangi D.C},
title = {Handwriting Analysis for Disease Identification},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {6},
Issue = {9},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {251-254},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2853},
doi = {https://doi.org/10.26438/ijcse/v6i9.251254}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i9.251254}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2853
TI - Handwriting Analysis for Disease Identification
T2 - International Journal of Computer Sciences and Engineering
AU - Syeda Asra, Shubhangi D.C
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 251-254
IS - 9
VL - 6
SN - 2347-2693
ER -

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Abstract

Handwriting is a tool to understand partially the unknown world of subconscious mind. The motor nerves come into play while writing. Personality trait identification can be done successfully with accuracy through handwriting. A research is done to show new avenues of application of handwriting analysis. Diseases like strokes, Alzheimer’s disease, Parkinson, Dyslexic disorders can be very easily diagnosed well in advance before the onset of the disease. A novel work is carried out to enlighten that, hand writing analysis not only identifies a person’s characteristic traits but also identifies many diseases including brain disorders like Alzheimer’s disease, suicidal tendency and pessimism etc.

Key-Words / Index Term

Behavior Recognition; Segmentation; SVM Classifier;Drop Fall Algorithm; Zernike Moments

References

[1] Syeda Asra, Dr.Shubhangi DC, “Personality Trait Identification – A Survey”, International Journal of Computer Science (IJCSN) , Vol 3, Issue 2 , pp.2277-5420, 2014.
[2] SyedaAsra, Dr.Shubhangi D.C ,”Personality Trait Identification Using Unconstrained Cursive and Mood Invariant Handwritten Text”I.J. Education and Management Engineering, 2015, 5, 20-31 Published Online October 2015 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2015.05.03
[3] Syeda Asra, Dr.Shubhangi DC ,” Specific Trait Identification in Margins Using Hand Written Cursive”, International Journal Of Engineering And Computer Science (IJECS) ISSN: 2319-7242, Volume 6 Issue 1 Jan. 2017, Page No. 19963-19964 Index Copernicus Value (2015): 58.10, DOI: 10.18535/ijecs/v6i1.19.
[4] Syeda Asra, Dr.Shubhangi D.C,” Human Behavior Recognition based on Hand Written Cursives by SVM Class/ifier, ”, in ICEECCOT Mysuru,2017.
[5] Syeda Asra, Dr.Shubhangi D.C,”Behaviour Recognition Based on Hand Written T-Letter Using SVM Classifier “ International Journal of Computer Science (IAENG) Scopus Indexed
[6] Jinyin Yang et.al.” A Novel Drop-fall Algorithm Based on Digital Features for Touching Digit Segmentation” IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON),2016.
[7] Tomoki Watanabe, Satoshi Ito, and Kentaro Yokoi,“ Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection”, Springer-Verlag Berlin Heidelberg 2009.
[8] Michael Vorobyov Notes on, Topic: “Shape Classification Using Zernike Moments”, iCamp at University of California Irvine August 5, 2016.