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Anuse, Alwin D.
- Recognition of Identical Shape Handwritten Devnagari Vowels
Authors
1 Electronics and Telecommunication Department, Maharashtra Institute of Technology, Pune, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 5 (2012), Pagination: 314-318Abstract
Devnagari is most popular script in India. There has been a significant improvement in the research related to the recognition of handwritten Devanagari characters in the past few years, but accurate recognition is a difficult task due to variations in shapes of the same character with different writer and identical shape characters. Most of the identical shape Devnagari characters are in vowels as compared to consonants. This paper proposes a novel method of recognizing identical shape handwritten Devnagari vowels. Recognition is carried out with two stage classifiers and multiple feature extraction methods. Support Vector Machine (SVM) based approach is suggested for pre-classification. For this pre-classifier chain code histogram features are used. Then vowels misclassified or confused with its identical vowel are found and grouped together. Each group vowels are applied to set of feature extraction methods specifically foreground pixel distribution, Intersection/junction features, chain code histogram features, and zone density features. These features are applied individually to second stage classifiers. Artificial neural network and SVM are used as second stage classifiers. Finally second stage classifiers outputs are combined with weighted majority voting scheme, for final decision. This approach of multi-stage classification improves the recognition rate of identical shape vowels to 94.61%.Keywords
Chain Code Histogram Features, Foreground Pixel Distribution, Identical Shape Devnagari Vowels, Intersection/Junction Features, Weighted Majority Voting, Zone Density Features.
- A Survey of Feature Extraction Methods for Handwritten Devnagari Character Recognition
Authors
1 Electronics and Telecommunication Department, Maharashtra Institute of Technology, Pune, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 1 (2012), Pagination: 26-30Abstract
In India, Devanagari script is used by more than 300 million people. There has been a significant improvement in the research related to the recognition of handwritten Devanagari characters in the past few years. But accurate handwritten character recognition is a difficult task due to variations in shapes of the same character with different writer. Selection of feature extraction is the most important factor in achieving high recognition performance in Optical Character Recognition (OCR) systems. This paper presents review on different feature extraction methods for off-line handwritten Devnagari OCR. The feature extraction methods are discussed based on Statistical, Structural, and Global transformation and moments. Along with feature-extraction methods Preprocessing, Segmentation, and Classification techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the recognition rates of these feature extraction methods and finally research scope in the Devnagari OCR is discussed. Moreover, the paper also contains an ample bibliography of many selected papers as an aid for the researchers working in the field of Devanagari OCR.