A study on one of the most important issues in a human action recognition task, i.e. how to create proper data representations with a high-level abstraction from large dimensional noisy video data, is carried out. Most of the recent successful studies in this area are mainly focused on deep learning. Deep learning methods have gained superiority to other approaches in the field of image recognition. In this survey, the authors first investigate the role of deep learning in both image and video processing and recognition. Owing to the variety and plenty of deep learning methods, the authors discuss them in a comparative form. For this purpose, the authors present an analytical framework to classify and to evaluate these methods based on some important functional measures. Furthermore, a categorisation of the state-of-the-art approaches in deep learning for human action recognition is presented. The authors summarise the significantly related works in each approach and discuss their performance.

In this research a hybrid feature selection technique based on genetic and simulated annealing algorithms is proposed. this approach is evaluated by using Bayesian classifier on a dataset of hand-printed Farsi characters which includes 100 samples for each 33 hand-printed characters. The acquired results have been improved by correction of Simulated Annealing through considering two minimum and maximum thresholds.

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