A Thresholded Gabor-CNN Based Writer Identification System for Indic Scripts

doi:10.1109/ACCESS.2021.3114799

A Thresholded Gabor-CNN Based Writer Identification System for Indic Scripts

2021-01-01

4

OA

AI

Muhammad Mostafa Monowar

M

Md. Abdul Hamid

DOI：10.1109/ACCESS.2021.3114799

Original

Original request for help

Save

Abstract

En 中文

Writer identification is the procedure of identifying individuals from handwriting. Writer identification is a common interest in biometric authentication and verification systems, and numerous studies are available for English, Chinese, Arabic, and Persian specific handwriting. This paper introduces a supervised offline Indic script writer identification system that can identify individuals using less than a single page of handwriting. A lightweight Convolutional Neural Network (CNN) architecture fused with non-trainable Gabor filters is used as an identification model that can recognize writers from scarce data. For the experiment, we used BanglaWriting dataset, which is openly available for Bengali writing and writer recognition. Further, we added Devanagari and Telugu datasets for evaluation. The overall evaluation shows that the proposed thresholded Gabor-based CNN architecture performs superior to numerous deep CNN architectures for Indic writer recognition.

Keywords:

Convolutional neural networks

Writing

Feature extraction

Neural networks

Image recognition

Deep learning

Task analysis

Image processing

convolutional neural network

Gabor filter

writer identification

Indic script

A Thresholded Gabor-CNN Based Writer Identification System for Indic Scripts

Abstract

Journal

Organization