This thesis aims to contribute to the research field of Video Optical Character Recognition (VOCR) by developing novel approaches that automatically detect and recognize embedded Arabic text in news videos. We introduce a two-stage method for Arabic text detection in video frames. In the first stage, which represents the CC-based detection part of this method, text candidates are firstly extracted, then filtered and grouped by respectively applying the Stroke Width Transform (SWT) algorithm, a set of heuristic rules and a proposed textline formation technique. In the second stage, which represents the machine-learning verification part, we make use of Convolutional Auto-Encoders (CAE) and Support Vector Machines (SVM) for text/non-text classification.For text recognition, we adopt a segmentation-free methodology using multidimensional Recurrent Neural Networks (MDRNN) coupled with a Connectionist Temporal Classification (CTC) decoding layer. This system includes also a new preprocessing step and a compact representation of character models. We aim in this thesis to stand out from the dominant methodology that relies on hand-crafted features by using different deep learning methods, i.e. CAE and MDRNNs to automatically produce features. Initially, there has been no publicly available dataset for artificially embedded text in Arabic news videos. Therefore, creating one is unquestionable. The proposed dataset, namely AcTiV, contains 189 video clips recorded from a DBS system to serve as a raw material for creating 4,063 text frames for detection tasks and 10,415 cropped text-line images for recognition purposes. AcTiV is freely available for the scientific community. It is worth noting that the dataset was used as a benchmark for two international competitions in conjunction with the ICPR 2016 and ICDAR 2017 conferences, respectively.