DocumentCode :
2727004
Title :
Empirical Evaluation of Character Classification Schemes
Author :
Neeba, N.V. ; Jawahar, C.V.
Author_Institution :
Centre for Visual Inf. Technol., Int. Inst. of Inf. Technol., Hyderabad
fYear :
2009
fDate :
4-6 Feb. 2009
Firstpage :
310
Lastpage :
313
Abstract :
In this paper, we empirically study the performance of a set of pattern classification schemes for character classification problems. We argue that with a rich feature space, this class of problems can be solved with reasonable success using a set of statistical feature extraction schemes. Experimental validation is done on a data set (of more than 500000 characters) collected and annotated from books printed primarily in Malayalam. Scope of this study include (a) applicability of a spectrum of classifiers and features (b) scalability of classifiers (c) sensitivity of features to degradation (d) generalization across fonts and (e) applicability across scripts.
Keywords :
character recognition; feature extraction; image classification; statistical analysis; character classification schemes; pattern classification schemes; statistical feature extraction schemes; Cellular neural networks; Classification tree analysis; Decision trees; Feature extraction; Information technology; Neural networks; Optical character recognition software; Pattern recognition; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Pattern Recognition, 2009. ICAPR '09. Seventh International Conference on
Conference_Location :
Kolkata
Print_ISBN :
978-1-4244-3335-3
Type :
conf
DOI :
10.1109/ICAPR.2009.41
Filename :
4782798
Link To Document :
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