Title: Accurate recognition of ancient handwritten Tamil characters from palm prints for the Siddha medicine systems

Authors: E.K. Vellingiriraj; P. Balasubrmanie

Addresses: Department of Computer Technology, Kongu Engineering College, Perundurai, 638 060, Erode, Tamil Nadu, India ' Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, 638 060, Erode, Tamil Nadu, India

Abstract: The ancient Tamil characters recognition is the complex task because there is no sufficient training information is available. Various researchers attempted to perform accurate recognition of ancient Tamil characters. In our preceding work, hybrid multi-neural learning based prediction and recognition system (HMNL-PRS) is introduced for the prediction process which lacks from inaccurate recognition. In this proposed research work, this is overcome by proposing the Brahmi character prediction and conversion system (BC-PCS) methodology. Here, the modified graph based segmentation algorithm (MGSA) is used to segment the characters. And then the statistical and structural features are extracted based on which classification is done using hybridised support vector machine based fuzzy neural network. In the MATLAB simulation environment, the proposed research work is implemented and it is confirmed that the proposed research work direct to give the excellent result compared to the preceding research methodology in terms of recognition rate.

Keywords: Brahmi characters; accurate recognition; segmentation; graph based approach; classification.

DOI: 10.1504/IJBIDM.2020.106134

International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.3, pp.345 - 360

Received: 26 Apr 2017
Accepted: 24 Sep 2017

Published online: 13 Feb 2020 *

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