Informatics Publishing Limited

Neeraj Hatekar ¹, Ashutosh Sharma ²

Affiliations
1 Center for Computational Social Sciences, University of Mumbai, Mumbai 400032, Maharashtra, IN
2 NIIT University, Neemrana, Alwar 301705, Rajasthan, IN

Abstract

This paper examines the structure of growth acceleration of the postindependence Indian economy using national as well as state level output figures and capital stock series at the national level. By examining the manner in which growth accelerations at various national and sub-national disaggregated series are clustered in time, we argue that they began in the 1980s and caught momentum in the 1990s. In moving away from excessive dependence on agriculture, Indian economy seems to have missed the Kuznets transition to an increased share of the manufacturing sector. This has important implications for employment and rural poverty.

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Ashutosh Sharma ¹, Nitin Kumar ¹, Adarsh Kumar ²

Affiliations
1 Mechanical Engineering, Global College of Engineering & Technology, Kahnpur Khui, Punjab, IN
2 Mechanical Engineering Department, Global College of Engineering & Technology, Kahnpur Khui, Punjab, IN

Abstract

Errors of measurement arise because our observations are affected by many sources of variability, but our conceptual frameworks necessarily ignore much of this variability. Sources of variability that are not included in our models and descriptions of phenomena are treated as error. A good theory of error supports the development of precise measurements, clearly defined constructs and sound public policy. Narrowly defined constructs that do not generalize much beyond the observed performances do not involve many sources of error, but constructs that generalize observed scores over a broad range of conditions of observation necessarily involve many potential sources of error. We can have narrow constructs with small errors or more broadly defined constructs with larger errors. Some errors that are negligible for individuals can have a substantial impact on estimates of group performance, and therefore, can have serious consequences.

Keywords

Measurement, Errors, Control.

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Ashutosh Sharma ¹

Affiliations
1 Department of Economics, University of Mumbai, Mumbai-400098, IN

Abstract

This study investigates the long-run neutrality of money using monthly data for India for the post-reform period 1991:1 to 2008:12. Seasonal integration and cointegration methodology have been applied on both narrow and broad money, real output and prices to test the money neutrality hypothesis. The empirical results of cointegration show that both narrow and broad money do effect real output at seasonal frequencies but at zero frequency, which can be interpreted as the long run, money does not affect real output. Money is neutral in the long run, but not in the short run. The cointegration result also confirms that both narrow and broad money affect prices at seasonal as well as at zero frequency.

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Ashutosh Sharma ¹, Rajiv K. Tayal ¹, Akhilesh Gupta ¹, Neelima Alam ¹, G. V. Raghunath Reddy ¹, Vineet Saini ¹, J. B. V. Reddy ¹, Ranjith Krishna Pai ¹, Ligy Philip ², Prasada Raju ³, T. Pradeep ⁴

Affiliations
1 Department of Science and Technology, New Delhi 110 016, IN
2 Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, IN
3 Industrial Consultancy and Sponsored Research, Indian Institute of Technology Madras, Chennai 600 036, India (former Scientist, DST), IN
4 Department of Chemistry, Indian Institute of Technology Madras, Chennai 600 036, IN

Abstract

No Abstract.

Keywords

No Keywords.

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Sukhbindra Singh Rawat ¹, Ashutosh Sharma ¹, Rachana Gusain ¹

Affiliations
1 Department of Computer Science, Doon University, IN

Abstract

A huge amount of information exists in the form of textbooks, paper documents, newspapers, and other physical forms, that is required to be digitized for its effective access and long-time availability. Optical Character Recognition (OCR) is an effective way to digitize the text. In this study, we have used Google’s Tesseract as the OCR tool. The focus of our study is to improve Tesseract’s accuracy on machine-printed Garhwali documents by using image pre-processing techniques including Super-Resolution (SR), different binarization methods (Otsu and adaptive thresholding), skew correction, morphological operations, and Image Magick methods. To improve the Tesseract results, we used the three proposed approaches – two approaches differed by the binarization method (Otsu and adaptive thresholding), and the third approach used ImageMagick methods for pre-processing. For evaluation purposes, we created a dataset by capturing images from a sample of five Garhwali textbooks using two mobile cameras with different resolutions; two books were captured by a high resolution camera and the other three were captured through a low resolution camera. Our experiments showed good results in specific cases, for high-resolution images, 88.13% accuracy was achieved for Otsu thresholding without applying the Super-Resolution and for low resolution images, 87.44% accuracy was achieved for Image Magick with Super-Resolution.

Keywords

Optical Character Recognition, Garhwali Language, Devanagari Script, Image Preprocessing, ImageMagick

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