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A Comparative Study of Machine Learning Approaches for Handwriter Identification

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

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A Comparative Study of Machine Learning Approaches for Handwriter Identification. / Durou, Amal; Aref, Ibrahim; Elbendak, Mosa et al.
2019. Paper presented at 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), London, United Kingdom.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Durou, A, Aref, I, Elbendak, M, Al-Maadeed, S & Bouridane, A 2019, 'A Comparative Study of Machine Learning Approaches for Handwriter Identification', Paper presented at 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), London, United Kingdom, 16/01/19 - 18/01/19. https://doi.org/10.1109/ICGS3.2019.8688032

APA

Durou, A., Aref, I., Elbendak, M., Al-Maadeed, S., & Bouridane, A. (2019). A Comparative Study of Machine Learning Approaches for Handwriter Identification. Paper presented at 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), London, United Kingdom. https://doi.org/10.1109/ICGS3.2019.8688032

Vancouver

Durou A, Aref I, Elbendak M, Al-Maadeed S, Bouridane A. A Comparative Study of Machine Learning Approaches for Handwriter Identification. 2019. Paper presented at 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), London, United Kingdom. doi: 10.1109/ICGS3.2019.8688032

Author

Durou, Amal ; Aref, Ibrahim ; Elbendak, Mosa et al. / A Comparative Study of Machine Learning Approaches for Handwriter Identification. Paper presented at 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), London, United Kingdom.6 p.

Bibtex

@conference{a406bdbed5fd4762bc8d59d43c6da8cf,
title = "A Comparative Study of Machine Learning Approaches for Handwriter Identification",
abstract = "During the past few years, writer identification has attracted significant interest due to its real-life applications including document analysis, forensics etc. Machine learning algorithms have played an important role in the development of writer identification systems demonstrating very effective performance results. Recently, the emergence of deep learning has led to various system in computer vision and pattern recognition applications. Therefore, this work aims to assess and compare the performance between one of the deep learning algorithms, AlexNet model, with two of the most effective machine learning classification approaches: Support Vector Machine (SVM) and K-Nearest-Neighbour (KNN). The evaluation has been conducted using both IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting.",
author = "Amal Durou and Ibrahim Aref and Mosa Elbendak and Somaya Al-Maadeed and Ahmed Bouridane",
year = "2019",
month = apr,
day = "10",
doi = "10.1109/ICGS3.2019.8688032",
language = "English",
note = "2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3) ; Conference date: 16-01-2019 Through 18-01-2019",
url = "https://ieeexplore.ieee.org/document/8688032",

}

RIS

TY - CONF

T1 - A Comparative Study of Machine Learning Approaches for Handwriter Identification

AU - Durou, Amal

AU - Aref, Ibrahim

AU - Elbendak, Mosa

AU - Al-Maadeed, Somaya

AU - Bouridane, Ahmed

PY - 2019/4/10

Y1 - 2019/4/10

N2 - During the past few years, writer identification has attracted significant interest due to its real-life applications including document analysis, forensics etc. Machine learning algorithms have played an important role in the development of writer identification systems demonstrating very effective performance results. Recently, the emergence of deep learning has led to various system in computer vision and pattern recognition applications. Therefore, this work aims to assess and compare the performance between one of the deep learning algorithms, AlexNet model, with two of the most effective machine learning classification approaches: Support Vector Machine (SVM) and K-Nearest-Neighbour (KNN). The evaluation has been conducted using both IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting.

AB - During the past few years, writer identification has attracted significant interest due to its real-life applications including document analysis, forensics etc. Machine learning algorithms have played an important role in the development of writer identification systems demonstrating very effective performance results. Recently, the emergence of deep learning has led to various system in computer vision and pattern recognition applications. Therefore, this work aims to assess and compare the performance between one of the deep learning algorithms, AlexNet model, with two of the most effective machine learning classification approaches: Support Vector Machine (SVM) and K-Nearest-Neighbour (KNN). The evaluation has been conducted using both IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting.

UR - https://ieeexplore.ieee.org/document/8688032

U2 - 10.1109/ICGS3.2019.8688032

DO - 10.1109/ICGS3.2019.8688032

M3 - Conference paper

T2 - 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3)

Y2 - 16 January 2019 through 18 January 2019

ER -