ISSN :2582-9793

Transfer Learning to Detect Age From Handwriting

Original Research (Published On: 29-Jun-2022 )
Transfer Learning to Detect Age From Handwriting
DOI : 10.54364/AAIML.2022.1126

Najla Alqawasmeh and Ching Y. Suen

Adv. Artif. Intell. Mach. Learn., 2 (2):394-406

Najla Alqawasmeh : Concordia University, Montreal, Quebec, Canada

Ching Y. Suen : Concordia University, Montreal, Quebec, Canada.

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DOI: 10.54364/AAIML.2022.1126

Article History: Received on: 25-May-22, Accepted on: 22-Jun-22, Published on: 29-Jun-22

Corresponding Author: Najla Alqawasmeh

Email: najla.alqawasmeh@gmail.com

Citation: Najla Alqawasmeh, Ching Y. Suen (2022). Transfer Learning to Detect Age From Handwriting. Adv. Artif. Intell. Mach. Learn., 2 (2 ):394-406

          

Abstract

    

Handwriting analysis is the science of determining an individual's personality from his or her handwriting by assessing features such as slant, pen pressure, word spacing, and other factors. Handwriting analysis has a wide range of uses and applications, including dating and socialising, roommates and landlords, business and professional, employee hiring, and human resources. In this research, the concept of transfer learning was explored utilizing two CNN architectures ResNet and GoogleNet as fixed feature extractors from handwriting samples. Then  SVM  was used to analyze the extracted features and classify the writer's gender and age. We built an Arabic dataset named FSHS to analyse and test the proposed system. In the gender detection system, applying the automatic feature extraction method to the FSHS dataset produced accuracy rates of 84.9\% and 82.2\% using ResNet and GoogleNet, respectively. While the age detection system using the automatic feature extraction method achieved accuracy rates of 69.7\% and 61.1\% using ResNet and GoogleNet, respectively.

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