Forensic analysis of questioned documents sometimes can be extensively data intensive. A forensic expert might
need to analyze a heap of document fragments and in such cases to ensure reliability he/she should focus only
on relevant evidences hidden in those document fragments. Relevant document retrieval needs finding of similar
document fragments. One notion of obtaining such similar documents could be by using document fragment's
physical characteristics like color, texture, etc. In this article we propose an automatic scheme to retrieve
similar document fragments based on visual appearance of document paper and texture. Multispectral color
characteristics using biologically inspired color differentiation techniques are implemented here. This is done
by projecting document color characteristics to Lab color space. Gabor filter-based texture analysis is used to
identify document texture. It is desired that document fragments from same source will have similar color and
texture. For clustering similar document fragments of our test dataset we use a Self Organizing Map (SOM)
of dimension 5×5, where the document color and texture information are used as features. We obtained an
encouraging accuracy of 97.17% from 1063 test images.
To take care of variability involved in the writing style of different individuals, a scheme for off-line Oriya isolated handwritten numeral recognition is presented here. Oriya is a popular script in India. The scheme is mainly based on features obtained from water reservoir concept as well as topological and structural features of the numerals. Reservoir based features like number of reservoirs, their size, heights and positions, water flow direction, topological feature like number of loops, centre of gravity positions of loops, the ratio of reservoir/loop height to the numeral height, profile based features, features based on jump discontinuity etc. are some of the features used in the recognition scheme. The proposed scheme is tested on 3550 data collected from different individuals of various background and we obtained an overall recognition accuracy of about 97.74%.
The main thrust of our research is to prepare a low-cost solar-selective absorber from an indigenous semiconducting mineral, galena (galena aggregate and galena concentrate), for a solar thermoelectric generator. We report the results of preparation and characterization of solar-selective coatings made from galena aggregate and galena concentrate collected from the Zawar mines in Rajasthan, India. The coatings of galena are prepared by a thermal evaporation technique and exhibit high absorptivity (α~0.95 and 0.97) in the solar spectral range and low emissivity (ε 375~0.21 and 0.27) in the thermal range. Finally, these coatings were compared with synthesized PbS coating prepared in our laboratory and found to be quite comparable. The structure and composition of the coatings were studied by x-ray diffraction and electron spectroscopy for chemical analysis. Reflectance and absorption studies were made in the 0.3- to 3.1-μm spectral range.
Conference Committee Involvement (3)
Document Recognition and Retrieval XXII
11 February 2015 | San Francisco, California, United States
Document Recognition and Retrieval XXI
5 February 2014 | San Francisco, California, United States
Document Recognition and Retrieval XX
5 February 2013 | Burlingame, California, United States
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