Comparison of Statistical and Structural Features for Handwritten Numeral Recognition

This paper compares the recognition accuracy of handwritten numerals achieved using statistical and structural features. Both features are trained and tested using neural network. In order to get good features, digit images are undergone various preprocessing activities. The operations such as noise...

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Bibliographic Details
Main Authors: Chacko, Binu P., Anto, P. Babu
Format: Conference Proceeding
Language:English
Subjects:
Character recognition
Feature extraction
Handwriting recognition
Image recognition
Joining processes
Neural networks
Pattern recognition
Spatial resolution
Surface morphology
Surface reconstruction
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Summary:This paper compares the recognition accuracy of handwritten numerals achieved using statistical and structural features. Both features are trained and tested using neural network. In order to get good features, digit images are undergone various preprocessing activities. The operations such as noise removal, thresholding, linking broken digit, rotation, pruning and cropping are done before feature extraction. The recognition rate obtained using statistical and structural features are 93.3% and 95.7% respectively.
DOI:10.1109/ICCIMA.2007.173