Deep Learning-Based Scene Text Detection

Dear Colleagues,

Scene text detection has emerged as a critical task within computer vision, driven by the growing demand for automated understanding of textual information in images. This capability has become increasingly important in applications such as autonomous driving, document analysis, video surveillance, and augmented reality, where extracting textual data from diverse, cluttered, and dynamic environments is essential. Here, the ability to detect and recognize text in natural scenes has significantly enhanced many automated systems, contributing to broader advancements in artificial intelligence and visual perception.

Deep learning techniques have substantially improved the accuracy and robustness of scene text detection. Convolutional Neural Networks (CNNs) have been widely used for feature extraction, facilitating efficient detection in both structured and unstructured environments. Recurrent Neural Networks (RNNs) aid in sequence modeling for improved text recognition, while transformer-based models enhance detection by leveraging global contextual information. These advances have made it possible to detect text across various challenging real-world scenarios.

Despite these improvements, scene text detection still presents several challenges. Complex backgrounds, occlusions, and variations in text appearance often impair detection accuracy. The ability to recognize curved or multi-oriented text remains a significant challenge, as many existing models struggle with arbitrary orientations. Additionally, factors such as low-resolution images, adverse weather conditions, and diverse fonts and languages further complicate detection efforts. The computational cost associated with state-of-the-art deep learning models also limits their deployment in real-time and edge computing environments.

To address these challenges, research has focused on improving the adaptability and efficiency of scene text detection models. Advances in representation learning, optimization strategies, and lightweight architectures have enabled better performance in diverse settings. Context-aware models enhance detection in cluttered environments, while knowledge transfer mechanisms and data-efficient learning approaches—such as self-supervised learning and few-shot learning—help reduce dependence on large labeled datasets. Additionally, efforts to improve computational efficiency are facilitating real-time applications in autonomous systems and accessibility tools.

This Special Issue serves as a platform for researchers worldwide to share their work and recent advancements in deep learning-based scene text detection. By fostering collaboration and knowledge exchange, it aims to drive progress in adaptability, efficiency, and scalability for real-world applications. The collection will explore methods that improve learning efficiency, generalization, and robustness while addressing existing challenges. Topics of interest include, but are not limited to, the following:

• CNN, transformer, and hybrid architectures for scene text detection;
• Image segmentation and super-resolution techniques for text detection;
• Context-aware and knowledge transfer-based detection models;
• Efficient and lightweight architectures for real-time applications;
• Advances in self-supervised and few-shot learning;
• Image restoration for improved scene text detection;
• Optimization techniques for improving robustness and efficiency;
• Data augmentation and synthetic text generation for enhanced training.

Dr. Qian Zhang
Dr. Ying Weng
Guest Editors

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• CNN, transformer, and hybrid architectures for scene text detection
• image segmentation and super-resolution techniques for text detection
• context-aware and knowledge transfer-based detection models
• efficient and lightweight architectures for real-time applications
• advances in self-supervised and few-shot learning
• image restoration for improved scene text detection
• optimization techniques for improving robustness and efficiency
• data augmentation and synthetic text generation for enhanced training