Open Access

ARTICLE

Efficient Scalable Template-Matching Technique for Ancient Brahmi Script Image

Sandeep Kaur*, Bharat Bhushan Sagar
Department of CSE, Birla Institute of Technology, Mesra, Ranchi, 835215, India
* Corresponding Author: Sandeep Kaur. Email:

Computers, Materials & Continua 2023, 74(1), 1541-1559. https://doi.org/10.32604/cmc.2023.032857

Received 31 May 2022; Accepted 05 July 2022; Issue published 22 September 2022

Abstract

Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars, stones, or leaves. Optical recognition systems can help in preserving, sharing, and accelerate the study of the ancient scripts, but lack of standard dataset for such scripts is a major constraint. Although many scholars and researchers have captured and uploaded inscription images on various websites, manual searching, downloading and extraction of these images is tedious and error prone. Web search queries return a vast number of irrelevant results, and manually extracting images for a specific script is not scalable. This paper proposes a novel multistage system to identify the specific set of script images from a large set of images downloaded from web sources. The proposed system combines the two most important pattern matching techniques-Scale Invariant Feature Transform (SIFT) and Template matching, in a sequential pipeline, and by using the key strengths of each technique, the system can discard irrelevant images while retaining a specific type of images.

Keywords

Brahmi script; SIFT (scale-invariant feature transform); multi-scale template matching; web scraping

Cite This Article

S. Kaur and B. B. Sagar, "Efficient scalable template-matching technique for ancient brahmi script image," Computers, Materials & Continua, vol. 74, no.1, pp. 1541–1559, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 137

    View

  • 140

    Download

  • 2

    Like

Share Link

WeChat scan