Exploration of Improved Methodology for Character Image Recognition of Two Popular Indian Scripts using Gabor Feature with Hidden Markov Model | Zendy

Shubhra Saxena | Zendy; Vijay Dhaka | Zendy

Open Access

Exploration of Improved Methodology for Character Image Recognition of Two Popular Indian Scripts using Gabor Feature with Hidden Markov Model

Author(s) -

Shubhra Saxena,

Vijay Dhaka

Publication year - 2015

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/20459-2817

Subject(s) - computer science , scripting language , character (mathematics) , pattern recognition (psychology) , artificial intelligence , feature (linguistics) , hidden markov model , image (mathematics) , gabor transform , character recognition , computer vision , natural language processing , linguistics , mathematics , philosophy , geometry , operating system , time–frequency analysis , filter (signal processing)

character recognition plays an important role in the modern world. It can solve more complex problems and make the human's job easier. The present work portrays a novel approach in recognizing handwritten cursive character using Hidden Markov Model (HMM) . The method exploits the HMM formalism to capture the dynamics of input patterns, by applying a Gabor filter to a character image, observation feature vector is obtained, and used to form feature vectors for recognition. The HMM model is proposed to recognize a character image. All the experiments are conducted by using the Matlab tool kit. KeywordsCharacter Recognition, Feature Extraction,

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