This study presents a novel approach for Arabic video text recognition based on recurrent neural networks. In fact, embedded texts in videos represent a rich source of information for indexing and automatically annotating multimedia documents. However, video text recognition is a non‐trivial task due to many challenges like the variability of text patterns and the complexity of backgrounds. In the case of Arabic, the presence of diacritic marks, the cursive nature of the script and the non‐uniform intra/inter word distances, may introduce many additional challenges. The proposed system presents a segmentation‐free method that relies specifically on a multi‐dimensional long short‐term memory coupled with a connectionist temporal classification layer. It is shown that using an efficient pre‐processing step and a compact representation of Arabic character models brings robust performance and yields a low‐error rate than other recently published methods. The authors’ system is trained and evaluated using the public AcTiV‐R dataset under different evaluation protocols. The obtained results are very interesting. They also outperform current state‐of‐the‐art approaches on the public dataset ALIF in terms of recognition rates at both character and line levels.

Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This paper describes a new well-defined and annotated Arabic-Text-in-Video dataset called AcTiV 2.0. The dataset is dedicated especially to building and evaluating Arabic video text detection and recognition systems. AcTiV 2.0 contains 189 video clips serving as a raw material for creating 4063 key frames for the detection task and 10,415 cropped text images for the recognition task. AcTiV 2.0 is also distributed with its annotation and evaluation tools that are made open-source for standardization and validation purposes. This paper also reports on the evaluation of several systems tested under the proposed detection and recognition protocols.