Recognition of Handprinted Bangla Numerals Using Neural Network Models
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Combining multiple classifiers based on third-order dependency for handwritten numeral recognition
Pattern Recognition Letters
Fuzzy model based recognition of handwritten numerals
Pattern Recognition
Automatic fuzzy rule base generation for on-line handwritten alphanumeric character recognition
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers from the KES2004 conference
Handwritten Numeral Recognition of Six Popular Indian Scripts
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Handwritten Devnagari Numerals Recognition with Higher Accuracy
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 03
Recognition of off-line handwritten devnagari characters using quadratic classifier
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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Automatic recognition of handwritten numerals is difficult because of the huge variety of ways in which people write. Attempts in the literature employ complicated features and recognition engines in trying to cope with the variety of symbols. But this makes the process slow. In this work, a hybrid technique is proposed to achieve the objective of recognition of handwritten Devanagari numerals with less time consumption and without sacrificing recognition accuracy. A database of 11,000 samples is created while ensuring that the samples include a variety of handwritings which are written with different writing instruments and in different colors. The features employed are density features and spline-based edge direction histogram features and combination thereof. The database size is reduced by using clustering to identify similar samples and putting only one representative sample in lieu of the whole cluster as well as reducing the number of features using PCA. This two-fold reduction provides a smaller database. A hybrid technique utilizing artificial Neural Networks A-NN, K-nearest neighbour K-NN and other learning methods is implemented to ensure higher recognition accuracy and speed. These ideas are put together to provide a fast and robust scheme for recognition of handwritten Devanagari numerals with high recognition accuracy, i.e., 99.40% at a reasonable speed.