Embedded Arabic text detection and recognition in videos. (Détection et reconnaissance de texte Arabe incrusté dans les vidéos)

This thesis focuses on Arabic embedded text detection and recognition in videos. Different approaches robust to Arabic text variability (fonts, scales, sizes, etc.) as well as to environmental and acquisition condition challenges (contrasts, degradation, complex background, etc.) are proposed. We introduce different machine learning-based solutions for robust text detection without relying on any pre-processing. The first method is based on Convolutional Neural Networks (ConvNet) while the others use a specific boosting cascade to select relevant hand-crafted text features. For the text recognition, our methodology is segmentation-free. Text images are transformed into sequences of features using a multi-scale scanning scheme. Standing out from the dominant methodology of hand-crafted features, we propose to learn relevant text representations from data using different deep learning methods, namely Deep AutoEncoders, ConvNets and unsupervised learning models. Each one leads to a specific OCR (Optical Character Recognition) solution. Sequence labeling is performed without any prior segmentation using a recurrent connectionist learning model. Proposed solutions are compared to other methods based on non-connectionist and hand-crafted features. In addition, we propose to enhance the recognition results using Recurrent Neural Network-based language models that are able to capture long-range linguistic dependencies. Both OCR and language model probabilities are incorporated in a joint decoding scheme where additional hyper-parameters are introduced to boost recognition results and reduce the response time. Given the lack of public multimedia Arabic datasets, we propose novel annotated datasets issued from Arabic videos. The OCR dataset, called ALIF, is publicly available for research purposes. To the best of our knowledge, it is the first public dataset dedicated for Arabic video OCR. Our proposed solutions were extensively evaluated. Obtained results highlight the genericity and the efficiency of our approaches, reaching a word recognition rate of 88.63% on the ALIF dataset and outperforming well-known commercial OCR engine by more than 36%.