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

• 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
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-018-00776-x

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.