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Performance enhancement of online handwritten Tamil symbol recognition with reevaluation techniques

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Abstract

In this article, we aim at reducing the error rate of the online Tamil symbol recognition system by employing multiple experts to reevaluate certain decisions of the primary support vector machine classifier. Motivated by the relatively high percentage of occurrence of base consonants in the script, a reevaluation technique has been proposed to correct any ambiguities arising in the base consonants. Secondly, a dynamic time-warping method is proposed to automatically extract the discriminative regions for each set of confused characters. Class-specific features derived from these regions aid in reducing the degree of confusion. Thirdly, statistics of specific features are proposed for resolving any confusions in vowel modifiers. The reevaluation approaches are tested on two databases (a) the isolated Tamil symbols in the IWFHR test set, and (b) the symbols segmented from a set of 10,000 Tamil words. The recognition rate of the isolated test symbols of the IWFHR database improves by 1.9 %. For the word database, the incorporation of the reevaluation step improves the symbol recognition rate by 3.5 % (from 88.4 to 91.9 %). This, in turn, boosts the word recognition rate by 11.9 % (from 65.0 to 76.9 %). The reduction in the word error rate has been achieved using a generic approach, without the incorporation of language models.

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Acknowledgements

The authors thank Technology Development for Indian Languages (TDIL), Department of Information Technology, Govt of India for funding this work. The help rendered by the staff of Medical Intelligence and Language Engineering (MILE) Laboratory in data collection and truthing is acknowledged.

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Authors and Affiliations

  1. Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, Assam, India

    Suresh Sundaram

  2. Medical Intelligence and Language Engineering Laboratory, Indian Institute of Science, Bangalore, India

    A. G. Ramakrishnan

Authors
  1. Suresh Sundaram
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  2. A. G. Ramakrishnan
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Corresponding author

Correspondence to Suresh Sundaram.

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Originality and contributions

1. In the literature, in the context of online Indic handwriting, there is hardly any comprehensive work that addresses the problem of disambiguating confused characters. To the knowledge of the authors, this may be a maiden attempt at reducing the error rate of online handwritten Tamil symbols with reevaluation strategies.

2. A dynamic time-warping approach has been proposed to capture the regions of the trace that discriminate confused Tamil symbols. Thereafter, novel class-specific discriminative features are proposed from the extracted regions to disambiguate these symbols.

3. Dedicated to each confusion set (derived from the confusion matrix), an SVM classifier (referred to as expert) has been proposed. The expert classifier operates on the novel discriminative features.

4. A set of novel features have been proposed to reduce the confusions of vowel modifiers in CV combinations.

5. A systematic study of the occurrence frequency of linguistically similar Tamil symbols has been performed on a text corpus.

Appendix: The complete list of Tamil characters

Appendix: The complete list of Tamil characters

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Sundaram, S., Ramakrishnan, A.G. Performance enhancement of online handwritten Tamil symbol recognition with reevaluation techniques. Pattern Anal Applic 17, 587–609 (2014). https://doi.org/10.1007/s10044-013-0353-7

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