Application specific voice interfaces in local languages will go a long way in reaching the benefits of technology to rural India. A continuous speech recognition system in Hindi tailored to aid teaching Geometry in Primary schools is the goal of the work. This paper presents the preliminary work done towards that end. We have used the Mel Frequency Cepstral Coefficients as speech feature parameters and Hidden Markov Modeling to model the acoustic features. Hidden Markov Modeling Tool Kit —3.4 was used both for feature extraction and model generation. The Julius recognizer which is language independent was used for decoding. A speaker independent system is implemented and results are presented
The current paper proposes recognition of partially invisible objects in images using image enhancement techniques. The problem mainly arises in night vision images which comprise poor contrast standards. Also during daytime, the object which is captured under sunlight is the lone survivor and the rest of information is not captured by camera properly. Image enhancement techniques to improve visual quality have been popularized with the proliferation of digital imagery and computers. Histogram Equalization (HE) is a versatile image improvement technique that can be incorporated for converting the partial visible objects/invisible objects into a proper vision. Further for enriching the information in image obtained by the HE image, a Contrast Limited Adaptive Histogram Equalization (CLAHE) is incorporated and finally for smoothing purpose, the image thus obtained is passed through a Gaussian filter. Results on various set of images show that above two techniques HE and CLAHE along with a Gaussian filter significantly improve the quality of image and hence assist to discover the partially visible/invisible objects.
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