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MultiDIAS: A Hierarchical Multi-layered Document Image Annotation System Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion

Authors : Maryam Asadzadeh Kaljahi, P. V. Vidya Varshini, Palaiahnakote Shivakumara, Umapada Pal, Tong Lu, D. S. Guru

Published in: Document Analysis and Recognition

Publisher: Springer Singapore

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Abstract

Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. This paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. Then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. This process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods.

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Metadata
Title
Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion
Authors
Maryam Asadzadeh Kaljahi
P. V. Vidya Varshini
Palaiahnakote Shivakumara
Umapada Pal
Tong Lu
D. S. Guru
Copyright Year
2019
Publisher
Springer Singapore
Book
Document Analysis and Recognition

Print ISBN: 978-981-13-9360-0

Electronic ISBN: 978-981-13-9361-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-9361-7_11

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