Multi-Oriented Real-Time Arabic Scene Text Detection With Deep Fully Convolutional Networks

Scene text detection is one of the raising aspects of Information and Communications Technology (ICT) field used by individuals in our daily life. Therefore, textual information detection aim to determinate text line coordinates in two fields: printed documents and real-world scenes images. One of the key points to the success of detecting text on both accuracy and precision values for printed documents is deep learning. However, real-world scenes images still face Challenges from recognizing text from the remaining shapes. In this paper, we propose a deep Fully Convolutional Networks (FCN) multi-oriented system to real-time localized text through an end-to-end trainable single network. For training and evaluating stages, we have used the freely available Arabic-Text-in-Video (AcTiV) dataset.