Real-Time English Text Recognition Using Lightweight AI in Wireless Communication Networks

In the era of pervasive wireless communication, the need for efficient and accurate text recognition systems is growing, especially for applications involving edge devices in resource-constrained environments. This paper proposes a lightweight AI-based approach for English text recognition, leveraging a hybrid model combining convolutional neural networks (CNN) and gated recurrent units (GRU). The model effectively handles noisy wireless signals by capturing both spatial and temporal features from modulated signals. We incorporate techniques such as pruning and depthwise separable convolution (DSC) to reduce the model's size, making it suitable for deployment in wireless communication systems. Experimental results demonstrate that the proposed model outperforms several state-of-the-art methods, including traditional modulation recognition and deep learning-based alternatives, in terms of both recognition accuracy and model efficiency, even in low signal-to-noise ratio (SNR) conditions. The proposed model offers a promising solution for real-time text recognition in wireless communication environments.