Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text ...
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One of the major challenges in speech synthesis and recognition is co-articulated unit segmentation. In this paper we present a novel technique for segmenting the basic co-articulated units using multifactorial analys...
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Any generic deep machine learning algorithm is essentially a function fitting exercise, where the network tunes its weights and parameters to learn discriminatory features by minimizing some cost function. Though the ...
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In medical image analysis, the cost of acquiring high-quality data and annotation by experts is a barrier in many medical applications. Most of the techniques used are based on a supervised learning framework and requ...
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This paper is concerned with research on OCR (optical character recognition) of printed mathematical expressions. Construction of a representative corpus of technical and scientific documents containing expressions is...
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This paper describes a portable diagnostic telecardiology system, aimed to benefit the rural people of a third world country like India. The designed system consists of two major blocks;the first one is required to be...
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Air writing provides a more natural and immersive way of interacting with devices, with the potential of having significant application in fields like augmented reality and education. However, such systems often rely ...
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Pose estimation of a pedestrian helps to gather information about the current activity or the instant behaviour of the subject. Such information is useful for autonomous vehicles, augmented reality, video surveillance...
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This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things(Io T) analytics problems targeted on 1-dimensional(1-D) sensor data. As feature recommendat...
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This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things(Io T) analytics problems targeted on 1-dimensional(1-D) sensor data. As feature recommendation is a major bottleneck for general Io Tbased applications, this paper shows how this step can be successfully automated based on a Wide Learning architecture without sacrificing the decision-making accuracy, and thereby reducing the development time and the cost of hiring expensive resources for specific problems. Interpretation of meaningful features is another contribution of this research. Several data sets from different real-world applications are considered to realize the proof-of-concept. Results show that the interpretable feature recommendation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in development time.
This paper aims at automatic recognition of online handwritten mathematical expressions written on an electronic tablet. The proposed technique involves two major stages: symbol recognition and structural analysis. A ...
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