Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel
- Title
- Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel
- Creator
- Mallappa S.; Dhandra B.V.; Mukarambi G.
- Description
- In this paper, hybrid texture features are proposed for identification of scripts of bi-lingual camera images for a combination of 10 Indian scripts with Roman scripts. Initially, the input gray-scale picture is changed over into an LBP image, then GLCM and HOG features are extracted from the LBP image named as LBGLCM and LBHOG. These two feature sets are combined to form a potential feature set and are submitted to KNN and SVM classifiers for identification of scripts from the bilingual camera images. In all 77,000-word images from 11 scripts each contributing 7000-word images. The experimental results have shown the identification accuracy as 71.83 and 71.62% for LBGLCM, 79.21 and 91.09% for LBHOG, and 84.48 and 95.59% for combined features called CF, respectively for KNN and SVM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Electrical Engineering, Vol-967, pp. 113-124.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- GLCM; HOG; KNN; LBGLCM; LBHOG; LBP; SVM
- Coverage
- Mallappa S., Department of P.G.Studies and Research in Computer Science, Gulbarga University, Gulbarga, India; Dhandra B.V., Department of Statistics, Christ (Deemed to Be University), Bengaluru, India; Mukarambi G., School of Computer Science, Department of Computer Science, Central University of Karnataka, Gulbarga, India
- Rights
- Restricted Access
- Relation
- ISSN: 18761100; ISBN: 978-981197168-6
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mallappa S.; Dhandra B.V.; Mukarambi G., “Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20008.