Depression is turning out to be a major psychiatric disorder from which many people in India and Netherlands alike are suffering. Depression is associated with tremendous costs to society in terms of decreased productivity and years lost due to disability. Specifically, for India, the social stigma associated with depression is a concern, which leaves many cases of depression untreated. In this project, we seek to develop an objective measure to diagnose and track depression. We also want to further elucidate the mechanisms underlying depression by combining state-of-the-art neuroscience with machine learning to detect depressogenic thinking online, as well as with computational modelling of cognition to develop more detailed theories about how depressogenic thinking is amplified and dampened by different interventions.

Text in natural scene and video images contain a lot of information which is useful for people’s daily life, such as the names of streets and shops, traffic information, etc. In multi-lingual and multi-script countries like India, information communication using multiple languages/scripts is quite common. Text detection and recognition in natural scene and video images is a challenging task due to variation of low contrast, complex background, multiple scripts, etc. Additionally, varying orientation of characters in a text line causes the recognition performance to suffer more. We present an efficient approach to detect and recognize multi-oriented text of Indic scripts in scene and video images. Text detection will be performed using spatial and texture analysis. For recognition, a path of sliding window is estimated from text regions for feature extraction. Finally, HMM-based technique will be applied for recognition. Preliminary experiment shows that the proposed method is effective in Indic script recognition.