Open Access

ARTICLE

An Unsupervised Writer Identification Based on Generating Clusterable Embeddings

M. F. Mridha¹, Zabir Mohammad², Muhammad Mohsin Kabir², Aklima Akter Lima², Sujoy Chandra Das², Md Rashedul Islam^3,*, Yutaka Watanobe⁴

1 Department of Computer Science and Engineering, American International University Bangladesh, Dhaka, 1229, Bangladesh
2 Department of Computer Science & Engineering, Bangladesh University of Business & Technology, Dhaka, 1216, Bangladesh
3 Department of Computer Science and Engineering, University of Asia Pacific, Dhaka, 1216, Bangladesh
4 Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, 965-8580, Japan

* Corresponding Author: Md Rashedul Islam. Email:

Computer Systems Science and Engineering 2023, 46(2), 2059-2073. https://doi.org/10.32604/csse.2023.032977

Received 03 June 2022; Accepted 15 December 2022; Issue published 09 February 2023

Abstract

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecture-based Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.

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Keywords

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