Kannan, Balakrishnan; Jomy, John; Pramod, K V(June 1, 2011)
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Abstract:
Handwritten character recognition is always a frontier
area of research in the field of pattern recognition and image
processing and there is a large demand for OCR on hand written
documents. Even though, sufficient studies have performed in
foreign scripts like Chinese, Japanese and Arabic characters, only
a very few work can be traced for handwritten character
recognition of Indian scripts especially for the South Indian scripts.
This paper provides an overview of offline handwritten character
recognition in South Indian Scripts, namely Malayalam, Tamil,
Kannada and Telungu
Description:
National Conference on Indian Language Computing, Kochi, Feb 19-20, 2011
Development of Malayalam speech recognition system is in its infancy stage; although many works have
been done in other Indian languages. In this paper we present the first work on speaker independent
Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient
and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with
different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and
female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5%
with the unseen data
Description:
International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
Kannan, Balakrishnan; Pramod, K V; Jomy, John(IEEE, March 23, 2011)
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Abstract:
Optical Character Recognition plays an important role
in Digital Image Processing and Pattern Recognition. Even
though ambient study had been performed on foreign languages
like Chinese and Japanese, effort on Indian script is still
immature. OCR in Malayalam language is more complex as it is
enriched with largest number of characters among all Indian
languages. The challenge of recognition of characters is even high
in handwritten domain, due to the varying writing style of each
individual. In this paper we propose a system for recognition of
offline handwritten Malayalam vowels. The proposed method
uses Chain code and Image Centroid for the purpose of
extracting features and a two layer feed forward network with
scaled conjugate gradient for classification
Description:
Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on
Kannan, Balakrishnan; Jomy, John; Pramod, K V(MECS, April , 2013)
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Abstract:
In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets
Description:
I.J. Image, Graphics and Signal Processing, 2013, 4, 53-59