Deep learning and recurrent connectionist-based approaches for Arabic text recognition in videos

This paper focuses on recognizing Arabic embedded text in videos. The proposed methods proceed without applying any prior pre-processing operations or character segmentation. Difficulties related to the video or text properties are faced using a learned robust representation of the input text image. This is performed using Convolutional Neural Networks and Deep Auto-Encoders. Features are computed using a multi-scale sliding window scheme. A connectionist recurrent approach is then used. It is trained to predict correct transcriptions of the input image from the associated sequence of features. Proposed methods are extensively evaluated on a large video database recorded from several Arabic TV channels.