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Articles by C. Lakshmi
Total Records ( 2 ) for C. Lakshmi
  C. Lakshmi , K. Thenmozhi , J.B.B. Rayappan and Rengarajan Amirtharajan
  In this study, implied an overture for acquainting Tamil Character which is handwritten. This refers the procedure of converting written Tamil font to printed one since it is a tedious to course the above mentioned owing to its deviated writing manner, dimension, angle of direction etc. Here, scanned images get pre-processed and subdivided into, first, paragraphs, then (paragraph) to lines, then (line) to words and finally (word) to separate glyph. This study coalesces structural plus categorization analysis and is determined to be extra proficient in support of outsized and composite sets. Recognition Efficiency is enhanced and this proposal generates finest outcomes apart from doing better than existing methods. Also this routine can be further extended to other Indian languages as well.
  C. Lakshmi and M. Ponnavaikko
  Problem statement: Kernel discriminative common vector (KDCV) was one of the most effective non-linear techniques for feature extraction from high dimensional data including images and text data. Approach: This study presented a new algorithm called Boosting Kernel Discriminative Common Vector (BKDCV) to further improve the overall performance of KDCV by integrating the boosting and KDCV techniques. Results: In BKDCV, the feature selection and the classifier training were conducted by KDCV and AdaBoost.M2 respectively. To reduce the dependency between classifier outputs and to speed up the learning, each classifier was trained in the different feature space which was obtained by applying KDCV to a small set of hard-to-classify training samples. The proposed method BKDCV possessed several appealing properties. First, like all Kernel methods, it handled non-linearity in a disciplined manner. Second by introducing pair-wise class discriminant information into discriminant criterion, it further increased the classification accuracy. Third, by calculating significant discriminant information, within class scatter space, it also effectively contracted with the small sample size problem. Fourth, it constituted a strong ensemble based KDCV framework by taking advantage of boosting and KDCV techniques. Conclusion: This new method was applied on extended Yale B face database and achieves better classification accuracy. Experimental results demonstrated the promising performance of the proposed method as compared to the other methods.
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