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Text Region Identification in Indian Street Scene Images Using Stroke Width Transform and Support Vector Machine

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Abstract

Detecting the presence of text in street scene images is a very crucial task for many applications and its complexity may vary from script to script due to the unique characteristics of each script. A technique to detect and localize text written in Devanagari script from scene images is presented in this paper. Initially, candidate regions are localized using low-level features like edge and colour. Due to the complex nature of scene images, these regions may contain irrelevant information. Stroke Width Transform (SWT) and geometric features are then extracted from these localized regions for correctly identifying the text regions. An efficient technique is proposed in this paper for the extraction of stroke width from dark text (foreground) on a light background as well as from light text (foreground) on dark background. Methods based on heuristic rules are inefficient for text and non-text identification due to the nonlinearity of extracted features. It has been observed that Support Vector Machines are the most popular and efficient classifiers for text/non-text classification. Also, an attempt is made here to explore other computationally less expensive classifiers like Bayesian due to its simplicity and Decision Tree due to its pure class partitioning power. Hence SVM, Bayesian and Decision Tree classifiers are used for the classification of text and non-text regions and the results are compared. An image dataset containing 1250 scene images has been created for experimentation. It is clear from the experimental results that the technique proposed in this paper outperforms some of the existing techniques in terms of accuracy.

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Acknowledgements

This work was supported by the Science and Engineering Research Board (SERB), New Delhi, India for non-commercial research under the Young Scientist Scheme (Start-up Grant YSS/2015/000812).

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Authors and Affiliations

  1. Department of Computer Engineering, MMCOE, Pune, India

    S. S. Shiravale

  2. Department of Computer Science and Engineering, GIT, Belagavi, India

    S. S. Sannakki

  3. Department of Computer Engineering, AIT, Pune, India

    R. Jayadevan

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  1. S. S. Shiravale
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  2. S. S. Sannakki
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  3. R. Jayadevan
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Correspondence to S. S. Shiravale.

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Shiravale, S.S., Sannakki, S.S. & Jayadevan, R. Text Region Identification in Indian Street Scene Images Using Stroke Width Transform and Support Vector Machine. SN COMPUT. SCI. 2, 357 (2021). https://doi.org/10.1007/s42979-021-00745-y

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