Title :
Multiple Classifiers for Unconstrained Offline Handwritten Numeral Recognition
Author :
Sharma, Pramod Kumar
Author_Institution :
Kuliza Technol., Ludhiana
Abstract :
In this work we propose an approach that includes use of multiple classifiers for unconstrained handwritten numeral recognition. The objective of the present work is to provide efficient and reliable techniques for recognition of handwritten numerals. Features used for classification of numerals are directional features. The classifiers used to solve the complex problem of digit recognition are multi-layer perceptron (MLP) classifier, Combinations of learning vector quantization (LVQ) classifier and K nearest neighbor (KNN) classifier. Outputs from these classifiers are further combined by using a discriminant function. Experiments and results show that the present method is robust for recognizing handwritten numerals.
Keywords :
handwritten character recognition; learning (artificial intelligence); multilayer perceptrons; vector quantisation; K nearest neighbor classifier; KNN classifier; LVQ classifier; MLP classifier; digit recognition; discriminant function; learning vector quantization; multilayer perceptron; multiple classifiers; unconstrained offline handwritten numeral recognition; Data mining; Feature extraction; Handwriting recognition; Image coding; Image segmentation; Multilayer perceptrons; Nearest neighbor searches; Neural networks; Pattern classification; Vector quantization;
Conference_Titel :
Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on
Conference_Location :
Sivakasi, Tamil Nadu
Print_ISBN :
0-7695-3050-8
DOI :
10.1109/ICCIMA.2007.69