Article: FAF-Text: English text detection based on feature selection and adaptive fusion mechanism Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.29 pp.91 - 109 Abstract: Text detection plays a vital role in applications like automated document analysis and scene understanding, yet achieving reliable accuracy in cluttered or low-contrast environments remains challenging. We propose FAF-Text, an English text detection framework that integrates adaptive feature filtering and multi-scale fusion to address these limitations. The filtering module employs gradient analysis to suppress noise and irrelevant patterns, while the fusion mechanism dynamically combines contextual and semantic features through attention-based learning. Evaluations on benchmark datasets demonstrate a 23% improvement in edge preservation and 18% enhancement in multi-scale recognition compared to existing methods. Ablation studies confirm the necessity of both modules, particularly under high-noise and low-resolution conditions, Furthermore, the framework's modular architecture ensures compatibility with multilingual OCR systems, offering a balance between computational efficiency and adaptability to complex text layouts. Inderscience Publishers - linking academia, business and industry through research

Title: FAF-Text: English text detection based on feature selection and adaptive fusion mechanism

Authors: Ruixia Huang; Yunpeng Ji

Addresses: School of Humanities, Zhujiang College, South China Agricultural University, Guangzhou 510980, China ' School of Humanities, Zhujiang College, South China Agricultural University, Guangzhou 510980, China

Abstract: Text detection plays a vital role in applications like automated document analysis and scene understanding, yet achieving reliable accuracy in cluttered or low-contrast environments remains challenging. We propose FAF-Text, an English text detection framework that integrates adaptive feature filtering and multi-scale fusion to address these limitations. The filtering module employs gradient analysis to suppress noise and irrelevant patterns, while the fusion mechanism dynamically combines contextual and semantic features through attention-based learning. Evaluations on benchmark datasets demonstrate a 23% improvement in edge preservation and 18% enhancement in multi-scale recognition compared to existing methods. Ablation studies confirm the necessity of both modules, particularly under high-noise and low-resolution conditions, Furthermore, the framework's modular architecture ensures compatibility with multilingual OCR systems, offering a balance between computational efficiency and adaptability to complex text layouts.

Keywords: English text; feature filtering; adaptive fusion; text detection.

DOI: 10.1504/IJICT.2025.147876

International Journal of Information and Communication Technology, 2025 Vol.26 No.29, pp.91 - 109

Received: 28 May 2025
Accepted: 13 Jun 2025
Published online: 05 Aug 2025 *

Title: FAF-Text: English text detection based on feature selection and adaptive fusion mechanism

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