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Gaurav Harit
Researcher at Indian Institute of Technology, Jodhpur
Publications - 74
Citations - 630
Gaurav Harit is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Image segmentation & Character (mathematics). The author has an hindex of 13, co-authored 73 publications receiving 523 citations. Previous affiliations of Gaurav Harit include Indian Institutes of Technology & Indian Institute of Technology Delhi.
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A survey on optical character recognition for Bangla and Devanagari scripts
Soumen Bag,Gaurav Harit +1 more
TL;DR: A review of OCR work on Indian scripts, mainly on Bangla and Devanagari—the two most popular scripts in India, and the various methodologies and their reported results are presented.
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Nearest neighbour classification of Indian sign language gestures using kinect camera
Zafar Ahmed Ansari,Gaurav Harit +1 more
TL;DR: A functional unobtrusive Indian sign language recognition system was implemented and tested on real world data and proposes a method for a novel, low-cost and easy-to-use application, for Indian Sign Language recognition, using the Microsoft Kinect camera.
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Recognition of Bangla compound characters using structural decomposition
TL;DR: The novelty of the approach lies in the formulation of appropriate rules of character decomposition for segmenting the character skeleton into stroke segments and then grouping them for extraction of meaningful shape components.
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Action Quality Assessment Using Siamese Network-Based Deep Metric Learning
TL;DR: This work proposes a new action scoring system termed as Reference Guided Regression (RGR), which comprises a Deep Metric Learning Module that learns similarity between any two action videos based on their ground truth scores given by the judges, and a Score Estimation Module that uses the resemblance of a video with a reference video to give the assessment score.
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An improved contour-based thinning method for character images
Soumen Bag,Gaurav Harit +1 more
TL;DR: A contour-based thinning method used for performing skeletonization of printed noisy isolated character images by using shape characteristics of text to get skeleton of nearly same as the true character shape.