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Digit, character, and word recognition of a particular script play a key role in the field of pattern recognition. These days, Optical Character Recognition (OCR) systems are widely used in commercial market in various applications. In recent years, there are intensive research studies on optical character, digit, and word recognition. However, only a limited number of works are offered for numeral, character, and word recognition of Persian scripts. In this paper, we have used deep neural network and investigated different versions of DensNet models and Xception and compare our results with the state-of-the-art methods and approaches in recognizing Persian character, number, and word. Two holistic Persian handwritten datasets, HODA and Sadri, have been used. For a comparison of our proposed deep neural network with previously published research studies, the best state-of-the-art results have been considered. We used accuracy as our criteria for evaluation. For HODA dataset, we achieved 99.72% and 89.99% for digit and character, respectively. For Sadri dataset, we obtained accuracy rates of 99.72%, 98.32%, and 98.82% for digit, character, and words, respectively.

Following the successful development of advanced driver assistance systems (ADAS), the current research directions focus on highely automated vehicles aiming at reducing human driving tasks, and extending the operational design domain, while maintaining a higher level of safety. Currently, there are high research demands in academia and industry to predict driver intention and understating driver readiness, e.g. in response to a “take‐over request” when a transition from automated driving mode to human mode is needed. A driver intention prediction system can assess the driver's readiness for a safe takeover transition. In this study, a novel deep neural network framework is developed by adopting and adapting the DenseNet, long short‐term memory, attention, FlowNet2, and RAFT models to anticipate the diver maneuver intention. Using the public “Brain4Cars” dataset, the driver maneuver intention will be predicted up to 4 s in advance, before the commencement of the driver's action. The driver intention prediction is assessed based on 1) in‐cabin 2) out‐cabin (road) and 3) both in‐out cabin video data. Utilizing K‐fold cross‐validation, the performance of the model is evaluated using accuracy, precision, recall, and F1‐score metrics. The experiments show the proposed DIPNet model outperforms the state‐of‐the‐art in the majority of the driving scenarios.

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