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.
View on arXiv@article{park2025_2504.05770,
title={ A Lightweight Multi-Module Fusion Approach for Korean Character Recognition },
author={ Inho Jake Park and Jaehoon Jay Jeong and Ho-Sang Jo },
journal={arXiv preprint arXiv:2504.05770},
year={ 2025 }
}