__P. D. Gujrati__
We use rigorous nonequilibrium thermodynamic arguments to establish that (i)
the nonequilibrium entropy S(T_{0}) of any system is bounded below by the
experimentally (calorimetrically) determined entropy S_{expt}(T_{0}), (ii)
S_{expt}(T_{0}) is bounded below by the equilibrium or stationary state (such
as the supercooled liquid) entropy S_{SCL}(T_{0}) and consequently (iii)
S(T_{0}) cannot drop below S_{SCL}(T_{0}). It then follows that the residual
entropy S_{R} is bounded below by the extrapolated S_{expt}(0)>S_{SCL}(0) at
absolute zero. These results are very general and applicable to all
nonequilibrium systems regardless of how far they are from their stationary
states.

__P. D. Gujrati__
We consider an isolated system in an arbitrary state and provide a general
formulation using first principles for an additive and non-negative statistical
quantity that is shown to reproduce the equilibrium thermodynamic entropy of
the isolated system. We further show that the statistical quantity represents
the nonequilibrium thermodynamic entropy when the latter is a state function of
nonequilibrium state variables; see text. We consider an isolated 1-d ideal gas
and determine its non-equilibrium statistical entropy as a function of the box
size as the gas expands freely isoenergetically, and compare it with the
equilibrium thermodynamic entropy S_{0eq}. We find that the statistical entropy
is less than S_{0eq} in accordance with the second law, as expected. To
understand how the statistical entropy is different from thermodynamic entropy
of classical continuum models that is known to become negative under certain
conditions, we calculate it for a 1-d lattice model and discover that it can be
related to the thermodynamic entropy of the continuum 1-d Tonks gas by taking
the lattice spacing {\delta} go to zero, but only if the latter is
state-independent. We discuss the semi-classical approximation of our entropy
and show that the standard quantity S_{f}(t) in the Boltzmann's H-theorem does
not directly correspond to the statistical entropy.

__Juhi Ameta__,
__Nisheeth Joshi__,
__Iti Mathur__
Machine Translation for Indian languages is an emerging research area.
Transliteration is one such module that we design while designing a translation
system. Transliteration means mapping of source language text into the target
language. Simple mapping decreases the efficiency of overall translation
system. We propose the use of stemming and part-of-speech tagging for
transliteration. The effectiveness of translation can be improved if we use
part-of-speech tagging and stemming assisted transliteration.We have shown that
much of the content in Gujarati gets transliterated while being processed for
translation to Hindi language.

__S. Padmavathi__,
__Manojna K. S. S__,
__S. Sphoorthy Reddy__,
__D. Meenakshy__
The Braille system has been used by the visually impaired for reading and
writing. Due to limited availability of the Braille text books an efficient
usage of the books becomes a necessity. This paper proposes a method to convert
a scanned Braille document to text which can be read out to many through the
computer. The Braille documents are pre processed to enhance the dots and
reduce the noise. The Braille cells are segmented and the dots from each cell
is extracted and converted in to a number sequence. These are mapped to the
appropriate alphabets of the language. The converted text is spoken out through
a speech synthesizer. The paper also provides a mechanism to type the Braille
characters through the number pad of the keyboard. The typed Braille character
is mapped to the alphabet and spoken out. The Braille cell has a standard
representation but the mapping differs for each language. In this paper mapping
of English, Hindi and Tamil are considered.

__Mallikarjun Hangarge__,
__K. C. Santosh__,
__Srikanth Doddamani__,
__Rajmohan Pardeshi__
In this paper, we use statistical texture features for handwritten and
printed text classification. We primarily aim for word level classification in
south Indian scripts. Words are first extracted from the scanned document. For
each extracted word, statistical texture features are computed such as mean,
standard deviation, smoothness, moment, uniformity, entropy and local range
including local entropy. These feature vectors are then used to classify words
via k-NN classifier. We have validated the approach over several different
datasets. Scripts like Kannada, Telugu, Malayalam and Hindi i.e., Devanagari
are primarily employed where an average classification rate of 99.26% is
achieved. In addition, to provide an extensibility of the approach, we address
Roman script by using publicly available dataset and interesting results are
reported.