Kannada Character Recognition System A Review

Intensive research has been done on optical character recognition ocr and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market, but most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of works on Indian language character recognition especially Kannada script among 12 major scripts in India. This paper presents a review of existing work on printed Kannada script and their results. The characteristics of Kannada script and Kannada Character Recognition System kcr are discussed in detail. Finally fusion at the classifier level is proposed to increase the recognition accuracy.Comment: 12 pages, 8 figure

Exercise Does Not Effect Context-dependent Episodic Memory

Memory has been shown to be strongly associated with the context in which it is encoded, suggesting that the context is central to the memory itself. However, the effect of exercise on context dependent object recognition is not fully known. We then set out to investigate the effect of exercise on context dependent object recognition. In Experiment 1 we showed that a context change reduced object recognition memory but did not significantly disrupt object recognition. In Experiment 2 we assessed whether exercise would the mitigate the effect of context change. We showed that exercise does not significantly improve object recognition nor did it mitigate the effect of context change on object recognition. These results suggest that a discrete context change can significantly disrupt retrieval of object recognition memory. Our results do not agree with the body of literature related to this topic, so further inquisition into these effects should be undertaken to confirm or refute the impact of exercise on contextual object recognition

Modeling Topic and Role Information in Meetings using the Hierarchical Dirichlet Process

Abstract. In this paper, we address the modeling of topic and role information in multiparty meetings, via a nonparametric Bayesian model called the hierarchical Dirichlet process. This model provides a powerful solution to topic modeling and a flexible framework for the incorporation of other cues such as speaker role information. We present our modeling framework for topic and role on the AMI Meeting Corpus, and illustrate the effectiveness of the approach in the context of adapting a baseline language model in a large-vocabulary automatic speech recognition system for multiparty meetings. The adapted LM produces significant improvements in terms of both perplexity and word error rate.

A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation. Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for document modeling. In this work, we show how to successfully apply and extend this model to the context of visual scene modeling. Specifically, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the hidden topic features by incorporating label information into the training objective of the model. We also describe how to leverage information about the spatial position of the visual words and how to embed additional image annotations, so as to simultaneously perform image classification and annotation. We test our model on the Scene15, LabelMe and UIUC-Sports datasets and show that it compares favorably to other topic models such as the supervised variant of LDA.Comment: 13 pages, 5 figure

Understanding the link between emotional recognition and awareness, therapy, and training : a thesis presented in partial fulfilment of the requirements for the degree of Doctorate in Clinical Psychology at Massey University, Manawatū, New Zealand

Therapy is an emotionally laden event, both for individuals seeking therapeutic intervention and the therapists who provide it. While the recognition of emotions in the general population has been a popular topic of research, very little research has been conducted into the emotional competencies, or more specifically, emotion recognition and awareness of therapists. In addition, there are few studies on the effectiveness of emotion recognition training for therapists’ emotional competencies, which is surprising given the innately emotional moments that clients and therapists experience during therapeutic work. This study aimed to address these gaps by investigating the association between emotional recognition, awareness, practice, and training. Fifty five therapists made up of clinical psychologists, counsellors, and a psychotherapist completed an online task that involved completion of a social-emotional orientated questionnaire and an emotion recognition task. Of these 55 participants, 26 completed an emotion recognition training before completing the same task again, two weeks later, while the remainder 29 participants were instructed to participate in no emotion recognition training. The results revealed that, compared to the no treatment condition, those who received emotion recognition training were more accurate in their recognition of emotions and also reported higher use of therapeutic emotional practice. Unexpectedly, participants who completed emotion recognition training reported less emotional awareness than the control group. Related to this, an inverse relationship was found between emotion recognition ability and self-reported emotional awareness, as well as the finding for some support for an inverse relationship between emotion recognition ability and self-reported use of emotional practice. There are two implications of this research; first, emotion recognition training increases therapists’ accuracy in emotion recognition, and second, therapists may need to be provided emotional practice feedback by an alternative form rather than through supervision or client outcome. This is due to an inverse relationship being found between participants’ actual and perceived emotional awareness. Therefore, future research into social-emotional practices and client outcomes will be advised to be considered. The limitations of the study and areas for future research are also discussed

Proposing a hybrid approach for emotion classification using audio and video data

Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%