Arabic-Latin Scene Text Detection based on YOLO Models

Machine learning and artificial intelligence have led to notable progress in the field of deep learning and the identification of text within natural scene images in the past few years. Despite notable advancements, the effectiveness of deep learning and text detection in natural scene images, particularly for Arabic language, is frequently constrained by the scarcity of comprehensive datasets containing various multilingual scripts. YOLO (You Only Look Once) is a widely used deep learning neural network that has gained immense popularity for its versatility in handling diverse machine learning tasks, primarily in the field of computer vision. The YOLO algorithm has progressively garnered recognition for its exceptional performance to solving a complex problems, noisy data, as well as overcoming various challenges encountered in real-world scenarios. Our experiments provide a concise examination of text detection algorithms based on convolutional neural networks (CNNs), especially different versions of the YOLO models using a data augmentation technique applied to "SYPHAX" dataset, our new dataset of multilingual scripts in the wild. The objective of this paper is to offer insights into the future of YOLO in the field, highlighting potential research directions that can enhance text detection systems.