A Lightweight Multi-Module Fusion Approach for Korean Character Recognition

• URL: http://arxiv.org/abs/2504.05770v1
• Date: Tue, 08 Apr 2025 07:50:19 GMT
• Title: A Lightweight Multi-Module Fusion Approach for Korean Character Recognition
• Authors: Inho Jake Park, Jaehoon Jay Jeong, Ho-Sang Jo,
• Abstract summary: SDA-Net is a lightweight and efficient architecture for robust single-character recognition.<n>It achieves state-of-the-art accuracy on challenging OCR benchmarks, with significantly faster inference.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Optical Character Recognition (OCR) is essential in applications such as document processing, license plate recognition, and intelligent surveillance. However, existing OCR models often underperform in real-world scenarios due to irregular text layouts, poor image quality, character variability, and high computational costs. This paper introduces SDA-Net (Stroke-Sensitive Attention and Dynamic Context Encoding Network), a lightweight and efficient architecture designed for robust single-character recognition. SDA-Net incorporates: (1) a Dual Attention Mechanism to enhance stroke-level and spatial feature extraction; (2) a Dynamic Context Encoding module that adaptively refines semantic information using a learnable gating mechanism; (3) a U-Net-inspired Feature Fusion Strategy for combining low-level and high-level features; and (4) a highly optimized lightweight backbone that reduces memory and computational demands. Experimental results show that SDA-Net achieves state-of-the-art accuracy on challenging OCR benchmarks, with significantly faster inference, making it well-suited for deployment in real-time and edge-based OCR systems.

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