A New Hybrid Wavelet Decomposition-based Networks for Script Identification in Scene Images

Palaiahnakote, Shivakumara; Pal, Umapada; Mansouri, Taha

Abstract

Script identification is challenging because of the unpredictable nature of the scene text. This paper presents a new model for achieving accurate script identification irrespective of intra and inter-class variations. The distinct features that represent the scene text of different scripts uniquely are extracted by fusing inception, which captures multi-scale features, and dense network, which captures fine-grained features. To strengthen the feature extraction, the proposed work uses wavelet decomposition, which enhances the fine details like edges in the images. Furthermore, for extracting text style, we propose a soft style attention module, which captures the unique style of scene text. The above modules are integrated as a hybrid model for accurate script identification. To evaluate the proposed model, we conducted comprehensive experiments on benchmark datasets, namely CVSI2015, SIW-13, and MLe2e, and combined datasets (combining distinct classes of all three benchmark datasets). The results of the proposed model on different datasets show that the performance is superior to the state-of-the-art methods in terms of accuracy.

• Journal Article Type: Article
• Acceptance Date: Dec 31, 2024
• Publication Date: Jan 24, 2025
• Deposit Date: Feb 21, 2025
• Publicly Available Date: Feb 25, 2025
• Journal: Artificial Intelligence and Applications
• Print ISSN: 2811-0854
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.47852/bonviewAIA52023569