Transfer learning based handwritten character recognition of tamil script using inception-V3 Model

Article type: Research Article

Authors: Gayathri, R.^{; *} | Babitha Lincy, R.

Affiliations: Department of ECE, Sri Venkateswara College of Engineering, Sriperumbudur, India

Correspondence: [*] Corresponding author. R. Gayathri, Associate Professor Department of ECE, Sri Venkateswara College of Engineering, Sriperumbudur, India. E-mail: rgayathri@svce.ac.in.

Abstract: The paper describes the excellent method to get first-rate accuracy and performance in the discipline of Tamil character recognition in a handwritten mode. However, the subject is still at a nascent stage and grossly lacks adequate accuracy in the Tamil language, even though several studies have been conducted within the discipline of handwritten character recognition. This paper draws the attention to the offline handwritten recognition for the Tamil language using the Inception-v3 based transfer learning method. The proposed work is conducted on the readily available HP Tamil handwritten character offline dataset (Hewlett-Packard Lab: hpl-tamil-iso-char-offline-1.0.). It reveals that with the suitable arrangement of transfer learning approach with Inception-v3, the pre-trained model can achieve the recognition accuracy of 93.1%, overtaking the former deep learning designs. The achieved accuracy is due to the use of a pre-trained version with transfer learning that regularly hastens the method of the training process on a new task. Overall, this results in higher accuracy and a more capable version.

Keywords: Handwritten character recognition, inception-v3, tamil language, transfer learning

DOI: 10.3233/JIFS-212378

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6091-6102, 2022