Character recognition using wavelet compression

Ali, Hayder Ibrahim; Ali, Younus; Hossain, Eklas; Sultana, Shirin

doi:10.1109/iccitechn.2010.5723900

Cited by 4 publications

References 7 publications

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MLPNN based handwritten character recognition using combined feature extraction

Katiyar

Mehfuz

2015

International Conference on Computing, Communication &Amp; Automation

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Statistical techniques for off-line character recognition are not flexible and adaptive enough for new handwriting constraints. Offline handwritten character recognition of English alphabets using a three layered feed forward neural network is presented in this paper. The proposed recognition system describes the evaluation of feed forward neural network by combining four different feature extraction approaches(box approach, diagonal distance approach, mean and gradient operations).The proposed recognition system performs well on the benchmark dataset CEDAR (Centre of Excellence for Document Analysis And Recognition).

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MLPNN based handwritten character recognition using combined feature extraction

Katiyar

Mehfuz

2015

International Conference on Computing, Communication &Amp; Automation

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Handwritten Digit Recognition for Two Digits Using CNN & Image Processing

Mantri¹,

Meshram²,

Kawale³

et al. 2022

2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)

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A hybrid recognition system for off-line handwritten characters

Katiyar

Mehfuz

2016

SpringerPlus

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Computer based pattern recognition is a process that involves several sub-processes, including pre-processing, feature extraction, feature selection, and classification. Feature extraction is the estimation of certain attributes of the target patterns. Selection of the right set of features is the most crucial and complex part of building a pattern recognition system. In this work we have combined multiple features extracted using seven different approaches. The novelty of this approach is to achieve better accuracy and reduced computational time for recognition of handwritten characters using Genetic Algorithm which optimizes the number of features along with a simple and adaptive Multi Layer Perceptron classifier. Experiments have been performed using standard database of CEDAR (Centre of Excellence for Document Analysis and Recognition) for English alphabet. The experimental results obtained on this database demonstrate the effectiveness of this system.

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Character recognition using wavelet compression

Cited by 4 publications

References 7 publications

MLPNN based handwritten character recognition using combined feature extraction

MLPNN based handwritten character recognition using combined feature extraction

Handwritten Digit Recognition for Two Digits Using CNN & Image Processing

A hybrid recognition system for off-line handwritten characters

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