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Feature combination for binary pattern classification

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Abstract

The paper presents a novel framework for large class, binary pattern classification problem by learning-based combination of multiple features. In particular, class of binary patterns including characters/primitives and symbols has been considered in the scope of this work. We demonstrate novel binary multiple kernel learning-based classification architecture for applications including such problems for fast and efficient performance. The character/primitive classification problem primarily concentrates on Gujarati and Bangla character recognition from the analytical and experimental context. A novel feature representation scheme for symbols images is introduced containing the necessary elastic and non-elastic deformation invariance properties. The experimental efficacy of proposed framework for symbol classification has been demonstrated on two public data sets.

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Notes

  1. Two conjuncts /ksha/ and /jya/ are also treated as basic consonant.

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Acknowledgments

Authors are thankful to Prof. BB Chaudhuri, ISI Kolkata, and Prof. S Ramamohan, MSU Baroda, for providing the Bangla and the Gujarati character/symbol data set. The work was supported under the project sponsored by DIT, Government of India.

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Authors and Affiliations

  1. Department of Electrical Engineering, IIT Delhi, New Delhi, India

    Ehtesham Hassan & Santanu Chaudhury

  2. School of Engineering, Shiv Nadar University, Gautam Buddha Nagar, Uttar Pradesh, India

    M. Gopal

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Correspondence to Ehtesham Hassan.

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Hassan, E., Chaudhury, S. & Gopal, M. Feature combination for binary pattern classification. IJDAR 17, 375–392 (2014). https://doi.org/10.1007/s10032-014-0224-9

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