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Odia character recognition: a directional review

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Abstract

Character recognition is one of the challenging tasks of pattern recognition and machine learning arena. Though a level of saturation has been obtained in machine printed character recognition, there still remains a void while recognizing handwritten scripts. We, in this paper, have summarized all the existing research efforts on the recognition of printed as well as handwritten Odia alphanumeric characters. Odia is a classical and popular language in the Indian subcontinent used by more than 50 million people. In spite of its rich history, popularity and usefulness, not much research efforts have been made to achieve human level accuracy in case of Odia OCR. This review is expected to serve a benchmark reference for research on Odia character recognition and inspire OCR research communities to make tangible impact on its growth. Here several preprocessing methodologies, segmentation approaches, feature extraction techniques and classifier models with their respective accuracies so far reported are critically reviewed, evaluated and compared. The shortcomings and deficiencies in the current state-of-the-art are discussed in detail for each stage of character recognition. A new handwritten alphanumeric character database for Odia is created and reported in this paper in order to address the paucity of benchmark Odia database. From the existing research work, future research paradigms on Odia character recognition are suggested. We hope that such a comprehensive survey on Odia character recognition will serve its purpose of being a solid reference and help creating high accuracy Odia character recognition systems.

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Authors and Affiliations

  1. School of Electrical Sciences, Indian Institute of Technology (IIT) Bhubaneswar, Bhubaneswar, Odisha, India

    Kalyan S. Dash, N. B. Puhan & G. Panda

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  1. Kalyan S. Dash
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  2. N. B. Puhan
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  3. G. Panda
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Correspondence to Kalyan S. Dash.

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Dash, K.S., Puhan, N.B. & Panda, G. Odia character recognition: a directional review. Artif Intell Rev 48, 473–497 (2017). https://doi.org/10.1007/s10462-016-9507-5

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  • DOI: https://doi.org/10.1007/s10462-016-9507-5

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