A Hybrid Approach towards Cost Effective Model for Handwritten Character Recognition | Zendy

Nupur Chauhan | Zendy; Manish Sharma | Zendy; Pooja Singh | Zendy

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A Hybrid Approach towards Cost Effective Model for Handwritten Character Recognition

Author(s) -

Nupur Chauhan,

Manish Sharma,

Pooja Singh

Publication year - 2014

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/16666-6657

Subject(s) - computer science , character (mathematics) , character recognition , speech recognition , artificial intelligence , pattern recognition (psychology) , image (mathematics) , geometry , mathematics

Handwritten character is gaining a lot of attention in the area of pattern recognition as its applications in various fields are increasing day by day. HCR system is providing us with a key factor to a paperless environment. Feature Extraction is a key part for a cost effective model for handwritten character recognition. Effective features improve the recognition rate and misclassification. A hybrid model provides better performance in comparison of the individual. Convolution neural networks are viewed to be more efficient to optimize the recognition ability of HCR system.

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