Abstract
Every character of the language having script is written using basic units called primitives. One or more number of primitives connected appropriately result in construction of a character. In the process of character construction identification of primitives is considered as high priority. Through this paper we propose a semi-automatic method for extracting features from primitives for their recognition and further Kannada characters’ construction. The primitives are recognized automatically by adopting the zone features and neighbor classifier. The feature vectors are obtained for all the primitives of Kannada character set and a knowledge base is created. We have used Euclidean distance measure to establish similarity between test input primitives and existing primitives present in the knowledge base for identifying the primitives in Kannada characters. The suggested methodology is tested for 11520 manually extracted primitive images. Average recognition accuracies observed is in the range of 75% to 100% for printed primitives. Application spreads in various verticals of automating literature like calligraphy, digitizing old manuscripts, multimedia teaching, Robot based assistance in handwriting, animation etc.
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Anami, B.S., Garag, D.S. (2019). A Semi-automatic Methodology for Recognition of Printed Kannada Character Primitives Useful in Character Construction. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_22
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DOI: https://doi.org/10.1007/978-981-13-9187-3_22
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