A novel nearest interest point classifier for offline Tamil handwritten character recognition

Handwritten character recognition is the most widely used branch of study in image pattern recognition. Tamil, the official language of Tamil Nadu in South India, Sri Lanka, Singapore and Malaysia, has a script which contains many loops and compound characters, with small differences between charact...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2020-02, Vol.23 (1), p.199-212
Main Authors: Ashlin Deepa, R. N., Rajeswara Rao, R.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Character recognition
Classifiers
Computer Science
Handwriting recognition
Machine learning
Object recognition
Pattern Recognition
Theoretical Advances
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Summary:Handwritten character recognition is the most widely used branch of study in image pattern recognition. Tamil, the official language of Tamil Nadu in South India, Sri Lanka, Singapore and Malaysia, has a script which contains many loops and compound characters, with small differences between character classes. Most of the research on offline Tamil handwritten character recognition system was done only on few character classes as it is very difficult to distinguish between minute dissimilarities of large character classes. It is important to design a complete recognition system that can process all character classes of Tamil and distinguish natural variability between inter-class images. Unlike conventional machine learning approaches for pattern recognition problems, we have proposed a nearest interest point classifier, which can choose sufficient and necessary subset of features from a variable length high dimensional feature vector. Since this is a practical problem, in this work, a study on image to image matching is included through feature analysis without using machine learning approaches. The proposed algorithm gave a good recognition accuracy for all the character classes on the standard database available for Tamil, HP Labs offline Tamil handwritten character database. Our proposed classifier produced a recognition accuracy of 90.2% while including the whole dataset. The method has been compared with the standard classifiers and has been proved to be a state-of-the-art performance in recognition of accuracy over the previous results given in the literature.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-018-00776-x