Efficient One-Stage Deep Learning for Text Detection in Scene Images

Text detection in natural images is a crucial task for extracting and recognizing valuable information, but it comes with significant challenges. Traditional image processing methods often rely on synthetic features, which may not be suitable for handling the diverse range of unstructured text scenarios encountered in real-world environments. To overcome this challenge, deep learning techniques have emerged as a powerful tool for adaptively learning and extracting features to digitize text. In this paper, we introduce a novel approach based on the YOLOv5 architecture for text detection. Our method leverages the strengths of YOLOv5, including its efficient and lightweight architecture, multi-scale prediction capability, and advanced data augmentation techniques. By employing these features, our proposed model can accurately identify regions in an image that are more likely to contain text. Through extensive experimentation on popular benchmark datasets, we have evaluated the performance of our proposed model. The results have demonstrated that our method outperforms state-of-the-art approaches in terms of both detection efficiency and accuracy. This indicates the effectiveness of our approach in handling the challenges posed by text detection in natural images.