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

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 introduce...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.132329-132341
Main Authors: Mridha, M. F., Ohi, Abu Quwsar, Shin, Jungpil, Kabir, Muhammad Mohsin, Monowar, Muhammad Mostafa, Hamid, Md. Abdul
Format: Article
Language:English
Subjects:
Artificial neural networks
convolutional neural network
Convolutional neural networks
Datasets
Deep learning
Feature extraction
Gabor filter
Gabor filters
Handwriting
Identification systems
Image processing
Image recognition
Indic script
Neural networks
Recognition
Task analysis
writer identification
Writers
Writing
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3114799