Abstract
Present work aims at analyzing the role of directional features for efficient recognition of printed Odia characters. The characteristics of Odia scripts that demand for separate rigorous OCR research are identified. Directional features are extracted by directional decomposition of character image and using fixed zoning. The zones are determined based on the input character patterns. Initial experiment with a modest size of 20 features are taken by considering 4 directions and 5 fixed zones yield very promising results. It is shown that these features yield nearly 95% recognition accuracy by multi-class SVM classifiers. High acuracy of recognition justifies the importance of directional decomposition which indirectly captures the stroke information of the script. In another experiment, we consider 164 dimensional feature vectors by taking zones for the entire image. The observation made here can prove to be useful in building an efficient OCR system for Odia characters.
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Mitra, C., Pujari, A.K. (2013). Directional Decomposition for Odia Character Recognition. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_28
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DOI: https://doi.org/10.1007/978-3-319-03844-5_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03843-8
Online ISBN: 978-3-319-03844-5
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